AI Skills in Demand: 25 Essential Skills Required for a Career in AI Engineering

Did you know? India's AI sector is projected to create over 2.3 million jobs by 2027. However, with only 1.2 million professionals available, more than 1 million roles may remain unfilled, highlighting a critical need for reskilling in AI engineering.

AI engineers in 2025 must invest in learning specialized skills to stay competitive in India’s growing tech space. Lately, India is experiencing a growing demand for AI engineers, with job postings for AI-related roles increasing by 33.39% year-over-year. This growth reflects the need to learn diverse skills, including building AI models, automating complex tasks, and driving innovation. 

In this blog, we’ll explore 25 key AI skills in demand that you need to stay competitive in the growing field of AI engineering. You'll also learn how these skills are shaping the future of technology.

Becoming a skilled AI engineer starts with mastering machine learning, data handling, and model building. upGrad’s AL & ML courses, offered by top 1% global universities include specializations in Data Science, Deep Learning, and NLP. They also offer expert mentorship and industry-level projects to help you excel in AI roles.

25 AI Skills in Demand for AI Engineers to Build in 2025

AI engineers are the architects behind intelligent systems that are reshaping sectors like healthcare, finance, retail, and manufacturing. Their role involves designing, deploying, and maintaining AI-driven products that automate processes and support critical business decisions.

The skills required for AI engineer roles now extend far beyond foundational programming. Employers prioritize candidates with hands-on experience, tool fluency, and a track record of building deployable solutions. Practical readiness often outweighs academic credentials during hiring evaluations.

Adding to this, only 2.5% of AI roles are currently open to junior professionals (NASSCOM, 2025), underscoring the importance of developing job-ready capabilities early. From cloud computing to natural language processing (NLP), the demand is strongest for engineers who can contribute from day one.

To help professionals stay ahead, upGrad offers industry-leading programs designed to build these in-demand skills:

• Advanced Certificate Program in Generative AI – IIIT Bangalore
• Master of Science in ML & AI – LJMU
• Post Graduate Certificate in Machine Learning and Deep Learning – IIIT Bangalore 

Below are the 25 most critical skills every AI engineer should invest in! 

1. Machine Learning Algorithms

Machine learning algorithms enable AI systems to learn from data and make decisions without explicit programming. This is one of the foundational skills for AI, especially for engineers designing models that solve business-specific problems. In 2025, companies will prioritize professionals with deep expertise in supervised and ensemble learning techniques.

Ensemble methods like Random Forests and AdaBoost combine multiple models to reduce variance or bias. They boost predictive performance in tasks like fraud detection and churn analysis. 

Decision trees, on the other hand, split datasets based on feature thresholds and can model non-linear patterns effectively, making them useful in credit scoring or recommendation systems. A strong grasp of these algorithm types is a must for building robust, real-world AI applications.

Relevant Applications:

• Identifying anomalies in transactions to detect and prevent fraud.
• Analyzing customer data to deliver personalized marketing strategies.
• Using data to anticipate equipment failures and reduce downtime.
• Leveraging patient data to assist in more accurate diagnoses.

Example:
Let’s say, as an AI engineer at Tata Consultancy Services (TCS), you're tasked with enhancing Century Supply Chain Solutions' logistics operations. You and your team develop VIZIV, an AI-powered platform integrating machine learning to provide real-time visibility and optimization across the supply chain. 

Thanks to the application of machine learning algorithms like ensemble methods, VIZIV helps your clients automate consolidation efforts. This results in a 35% reduction in ocean freight costs and more efficient logistics management.

Also Read: Top Machine Learning Algorithms - Real World Applications & Career Insights

2. Deep Learning & Neural Networks

Deep learning, a crucial skill for becoming an AI engineer, enables systems to process and interpret complex data like images, audio, and natural language. This is made possible through different types of neural networks tailored to specific data types and tasks.

Convolutional Neural Networks (CNNs) are designed for image data and excel in tasks like image classification, object detection, and facial recognition. They’re widely used in industries like healthcare (e.g., X-ray analysis) and automotive (e.g., vision in self-driving cars). Recurrent Neural Networks (RNNs) are better suited for sequential data and are commonly applied in time series forecasting, speech recognition, and language modeling. 

While CNNs dominate computer vision, RNNs and their successors like LSTMs and GRUs perform better in NLP and financial time-series applications. Understanding the strengths of each helps AI engineers choose the right architecture for each domain-specific challenge.

Relevant Applications:

• Analyzes X-rays and MRIs for faster, more accurate diagnoses with reduced error.
• Enables real-time navigation and obstacle avoidance in self-driving cars using CNNs and RNNs.
• Power voice assistants with speech recognition to enhance human-computer interaction.
• Detects harmful text, images, and video content to ensure platform compliance.

Example:
Consider you’re working as an AI engineer at Wipro and tasked with improving healthcare diagnostics. You develop a deep learning system that analyzes X-rays and MRIs, helping hospitals in India spot conditions like pneumonia or cancer early on. Using CNNs, the system can identify these issues with high accuracy.

Because of this, diagnostic errors drop by 30%, meaning patients get more reliable results. On top of that, the system makes healthcare more accessible in rural areas, allowing people to get timely treatment. 

This is an excellent example of how deep learning makes a real difference, not just in tech, but in improving lives too.

Deepen your understanding of neural networks with upGrad’s Fundamentals of Deep Learning and Neural Networks Free Course. Explore topics such as model training and AI applications, enhancing your ability to develop sophisticated AI solutions.

3. Python Programming

Python is the preferred language for AI due to its simplicity and rich libraries like TensorFlow and PyTorch. Proficiency in Python enables rapid prototyping, testing, and deployment of AI models. Python fluency is one of the AI skills in demand due to its flexibility, community support, and integration with all major AI frameworks.

In 2025, Python expertise is required in 71% of AI job postings, highlighting its meaningful role in AI. Learning Python is one of the most practical skills for AI development, especially when working on fast iterations and production-ready models.

Relevant Applications:

• Python’s libraries like TensorFlow, PyTorch, and Scikit-learn are widely used to build powerful AI models for tasks like predictive analytics.
• It allows you to automate data cleaning, transformation, and preprocessing, which speeds up the entire workflow.
• Tools like Matplotlib and Seaborn in Python help engineers visualize data trends and model outputs.
• Helps integrate AI models into web and mobile applications.

Case Study:
Surya OCR, an open-source tool developed by Vik Paruchuri, gained attention in 2025 for its ability to process documents in over 90 languages. Built with Python and PyTorch, it performs better than traditional tools like Tesseract and commercial services like Google Cloud Vision.

A key strength of Surya OCR is its ability to detect and analyze document layouts, such as tables, images, and headers. Its seamless integration with Python 3.9+ and PyTorch ensures easy installation, and users can access its powerful features via the command-line interface or API for various OCR tasks.

If you want to enhance your skills as an AI engineer, begin by learning coding with upGrad’s Programming with Python: Introduction for Beginners. This course introduces programming concepts, data structures, and object-oriented programming, essential for AI development.

Also Read: Top 50 Python AI & Machine Learning Open-source Projects

4. Data Structures and Algorithms

Data structures and algorithms are essential for building scalable and efficient AI systems. This is one of the key skills used to become an AI engineer, especially when designing models for real-time or high-volume environments.

For instance, Trees and graphs are ideal for hierarchical or networked data in use cases like recommendation engines or route optimization. Hash tables, on the other hand, are widely used in search algorithms and database indexing, allowing constant-time data access critical in NLP and fraud detection pipelines.

Selecting the right structure for a given task improves performance, memory usage, and execution speed. By mastering these techniques, AI engineers can build robust solutions that operate reliably even under large-scale or complex data loads.

Relevant Applications:

• Use hash tables and trees for rapid data retrieval and personalized suggestions.
• Use graph algorithms to detect fraud or optimize social network connections.
• Employ efficient data structures during neural network training to minimize resource consumption.

Example:

Swiggy, India’s leading food delivery platform, optimized its delivery system using advanced data structures and algorithms. Swiggy used graph-based algorithms and hash tables to improve real-time order routing to match orders with nearby drivers and find the fastest routes.

This approach reduced delivery times, especially during peak hours, and improved customer satisfaction. By scaling its operations efficiently, Swiggy optimized resource use and maintained performance during high-demand periods, ensuring faster deliveries.

Strengthen your problem-solving abilities with upGrad’s Data Structures and Algorithms Course. Learn essential data structures and algorithms, such as arrays, queues, stacks, and sorting techniques, to optimize your code and enhance computational efficiency.

Also Read: Explore the Top 30+ DSA projects with source code in 2025

5. Statistics and Probability

Being proficient in statistics and probability is a prerequisite for AI engineer skills. These concepts help interpret data, check models' accuracy, and deal with uncertainty. Engineers use these skills to design experiments, choose the right metrics to measure success, and make data-based decisions.

Bayesian inference allows engineers to integrate prior knowledge into models and update outcomes as new data arrives, especially useful in medical diagnosis and fraud detection. Hypothesis testing, including t-tests and chi-square tests, helps validate model assumptions and performance across different datasets.

Hence, knowing statistics inside out is more important than ever for building trustworthy AI systems that can predict and generalize reliably under uncertainty.

Relevant Applications:

• Use statistical methods to validate product changes and measure their impact on user behavior.
• Apply significance tests to determine the accuracy of predictions across different datasets.
• Build probabilistic models to assess and mitigate financial and insurance risks.

Example:

Imagine you're working for a bank looking to improve its credit scoring system. To ensure the new model is accurate and reliable, you use regression analysis to assess how different factors (like income, credit history, etc.) impact a person's loan repayment ability.

You might also apply hypothesis testing to validate whether the new scoring model is better than the previous one. Additionally, you use probabilistic models such as logistic regression to estimate the likelihood of loan defaults. 

Using these statistical techniques, you help the bank reduce loan defaults by 12% and improve customer segmentation, making the credit scoring system more effective and reliable.

Want to explore statistics and probability in detail? Build a strong foundation in statistics with upGrad’s Free Inferential Statistics Course. Learn core concepts like probability, distributions, and expected values, essential for making sense of data in AI and analytics.

6. Linear Algebra and Calculus

Linear algebra and calculus are key mathematical tools for AI engineers. They help optimize neural networks, tune model parameters, and design algorithms that improve system intelligence. In practice, they allow engineers to break down complex problems into manageable steps, driving both efficiency and accuracy in AI performance.

For example, matrix factorization, a linear algebra technique, plays a crucial role in dimensionality reduction methods like PCA and recommendation systems such as Netflix’s movie suggestions. Similarly, gradient descent, a core concept in calculus, is used to train deep learning models by adjusting model weights to iteratively minimize prediction errors.

Relevant Applications:

• Use gradient descent to optimize model weights and minimize error during training.
• Apply Principal Component Analysis (PCA) to reduce data complexity without sacrificing essential information.
• Employ Fourier transforms for image and audio analysis, improving the precision of AI-driven content recognition.

Example:

Imagine you're building a credit risk model for a digital lending platform serving small businesses. With over 300 data points per applicant, your team uses PCA to simplify the dataset, identifying the most relevant financial indicators without losing predictive power.

During model training, you apply gradient descent to fine-tune weights and minimize prediction error. The final model reduces false positives by 17%, helping the underwriting team approve good borrowers faster and more confidently.

Also Read: Linear Algebra for Machine Learning: Critical Concepts, Why Learn Before ML

7. Computer Vision

Computer vision is one of the top AI skills in demand, especially for roles in autonomous vehicles, healthcare imaging, and intelligent surveillance. It is also one of the key skills required for AI engineer roles due to its broad applicability across industries. 

AI engineers use YOLO for real-time object detection, such as spotting vehicles or pedestrians in self-driving systems. For image classification and recognition, architectures like ResNet and Inception enhance model accuracy by enabling deeper networks and efficient feature extraction. 

Computer vision applications, from diagnosing diseases to automating inspections, rely on tools like OpenCV, TensorFlow, and Convolutional Neural Networks (CNNs) for real-time AI solutions. 

Relevant Applications:

• Detect unusual activity, recognize faces, and track movements in real-time to enhance safety.
• Analyze X-rays, MRIs, or CT scans to assist doctors in identifying tumors, fractures, or abnormalities early.
• Use cameras and vision models to track product placement, stock levels, and shelf compliance.
• Automatically detect defects in products on assembly lines, reducing manual checks and improving accuracy.

Case Study:

Radiology departments across India have long struggled with high patient loads and a shortage of trained specialists, especially in rural areas. Indian researchers developed an AI system using Vision Transformers and U-Net++ to detect 75 chest pathologies from X-rays, achieving up to 98% precision and 99.9% negative predictive value (NPV).

Deployed across 17 healthcare systems, the AI scanned over 150,000 X-rays, averaging 2,000 daily, dramatically reducing diagnostic delays. This improved early detection, eased radiologist workload, and brought high-quality diagnostics to underserved regions.

Read more: Computer Vision Engineer Salary in India: Career Path and Key Skills for 2025

8. Natural Language Processing (NLP)

NLP ranks among the top five AI skills in demand, powering applications like chat automation, voice search, and multilingual content processing. It is also one of the core skills required to become an AI engineer, especially in roles involving language-driven interactions. 

Recent advancements through transformer-based models like BERT and GPT have transformed language understanding, enabling high-accuracy tasks like summarization, semantic search, and contextual text generation.

In healthcare, NLP supports the analysis of patient records, extraction of clinical insights, and mining of research literature. To build such systems, AI engineers must be proficient in tools like TensorFlow, PyTorch, spaCy, and Hugging Face Transformers.

Relevant Applications:

• Develop chatbots and voice assistants that understand and respond to user queries in real-time.
• Analyze user sentiments to gauge public opinion on products, services, or events.
• Create tools that condense lengthy documents and translate text between languages accurately.
• Implement systems that handle customer inquiries, reducing response times and operational costs.

Case Study:

Bajaj Finance faced the challenge of managing millions of customer queries and scaling personalized engagement without ballooning operational costs. To solve this, the company deployed NLP-powered chatbots and GenAI assistants. These tools understand user intent, guide loan applications, and automate eKYC across multiple languages.

The solution used large language models to deliver hyper-personalized conversations, expanding outreach from 160 million to 500 million users. It resulted in ₹150 crore in annual cost savings and is projected to 3× increase direct-sales conversions, while reducing reliance on call centers.

Explore how NLP powers real-world applications with upGrad’s Free Introduction to Natural Language Processing Course. Get familiar with key tools like spaCy and Transformers, and start building AI systems that understand language like humans.

9. Reinforcement Learning

Reinforcement Learning (RL) teaches AI agents to make sequential decisions by rewarding desired outcomes. It powers adaptive systems like game-playing bots, autonomous robots, and dynamic pricing engines by enabling learning through trial and error. A core concept in RL is the exploration-exploitation trade-off, where agents must balance trying new actions (exploration) with optimizing known strategies (exploitation). 

For example, in gaming, AlphaGo explored countless move combinations before learning optimal Go strategies. In robotics, warehouse bots use RL to explore efficient picking paths before settling on the fastest routine. Similarly, recommendation systems may initially suggest diverse products to users before narrowing suggestions based on successful past clicks.

Common tools include OpenAI Gym, PyTorch-based RL libraries, Ray RLlib, and TensorFlow Agents. Engineers use algorithms like  Q-learning, SARSA, and policy gradients to train agents in simulated and real environments.

Relevant Applications:

• Training game-playing bots (e.g., chess or video games) using trial-and-error feedback.
• Enabling robots to navigate environments and manipulate objects autonomously.
• Optimizing logistics (like delivery routing) and industrial control systems.
• Managing dynamic systems (such as inventory or energy grids) through adaptive policies.

Example:

Zypp Electric, an Indian EV logistics startup, applied RL to route planning and battery management. Their AI agents learned optimal delivery routes over time, cutting delivery times by 27% and improving battery efficiency. 

This example shows RL’s real-world impact by rewarding quicker deliveries, Zypp’s system adapts routes dynamically, showcasing how RL can drive efficiency in urban mobility.
Deepen your understanding of reinforcement learning with upGrad’s Executive PG Programme in Machine Learning & AI. This program offers comprehensive modules on reinforcement learning, equipping you to develop AI models that learn optimal actions through trial and error.

10. Generative AI Techniques (GANs, Diffusion Models)

Generative AI includes models that create new content, like images, text, or audio. It does this by learning data patterns. Key techniques include GANs, where two neural networks compete to create realistic outputs, and Diffusion Models, which turn noise into detailed images step by step.

Generative Adversarial Networks (GANs) consist of a generator that creates synthetic data and a discriminator that evaluates its authenticity. Through this adversarial process, GANs learn to produce highly realistic outputs. They’re used in deepfakes, image super-resolution, and synthetic data generation for model training.

Diffusion Models, on the other hand, reverse a gradual noise process to produce crisp images or videos. Unlike GANs, which can struggle with stability, diffusion models are known for producing higher-quality, more detailed outputs. Their rising popularity in fields like art, design, and video synthesis is due to this improved fidelity and consistency.

Common tools include TensorFlow, PyTorch, Hugging Face Diffusers, RunwayML, and Google’s Imagen API, all required for developing modern generative systems.

Relevant Applications:

• Creating realistic images or artwork from text descriptions for design and marketing.
• Generating synthetic training data (e.g., medical scans or rare event images) to augment datasets.
• Producing new music, speech, or sound effects with AI-driven audio synthesis.
• Automatically generating game assets or visual effects in media production.

Case Study:

Intelliflicks Studios (Chandigarh) is pioneering generative cinema. By 2025, it plans to release a full-length Bollywood film made almost entirely with AI generative spectrum. The team uses AI to generate characters, scenes, and objects, feed them into video models, and synthesize audio with lip-synced dialogue.

This project highlights generative AI’s power in creative media. Trained on India-specific data, the models produce novel movie visuals, demonstrating future potential for AI-crafted content.

Learn how generative models like GANs and diffusion networks transform design, media, and synthetic data with upGrad’s  Free Generative AI Course. Understand the core techniques driving AI-generated visuals, audio, and creative content in today’s tech landscape.

Also Read: How Does Generative AI Work? Key Insights, Practical Uses, and More

11. Big Data Tools (Spark, Hadoop)

When you're working with massive datasets, traditional tools just don't cut it. That’s where Hadoop and Spark come in. Hadoop handles storage and batch processing with HDFS, MapReduce, and Hive. Spark speeds things up with in-memory processing and libraries like Spark SQL, MLlib, and Streaming.

As an AI engineer, you’ll use these tools to scale data pipelines and model training across clusters. Most beginners start with PySpark or Spark SQL, and explore tools like Hive, Kafka, or cloud versions like Amazon EMR and Azure HDInsight. For real-time processing, platforms like Apache Flink are also gaining traction.

Relevant Applications:

• Building ETL pipelines to ingest and process large user or sensor logs.
• Training machine learning models on distributed clusters using Spark MLlib.
• Real-time analytics (e.g., fraud detection) using Spark Streaming or Kafka.
• Data warehousing and interactive queries over massive datasets (Hive, Presto).

Case Study:

A leading Indian private-sector bank collaborated with consulting firm Oneture to build a cloud-based big data analytics platform. The solution used Apache Spark and Hadoop on AWS. It created a centralized analytics and machine learning data lake, powering use cases like end-to-end customer 360 profiles and fraud analysis. 

For example, the bank’s platform automates detecting suspicious Aadhaar payment transactions, drastically reducing manual effort. This shows how Spark/Hadoop enables scalable AI pipelines for real-world enterprise needs.

Also Read: Explore the Top 10 Big Data Tools for Businesses

12. Cloud Platforms (AWS, GCP, Azure)

Cloud platforms like AWS, GCP, and Azure give AI engineers on-demand access to compute, storage, and ML tools, without needing local servers. You can train models on GPUs, store data securely, and deploy AI services at scale.

Start with tools like SageMaker, Azure ML Studio, or Google AI Platform to train and serve models. These platforms also offer ready-to-use APIs for vision, language, and AutoML. Cloud setups support global teams, elastic scaling, and tools like Terraform or CI/CD pipelines to streamline AI workflows.

Relevant Applications:

• Training deep learning models on GPU clusters (e.g., AWS EC2 P3 instances or GCP TPUs).
• Deploying AI-driven web services (e.g., image recognition or chatbots) via cloud APIs and serverless functions.
• Storing large datasets in data lakes (Amazon S3, Google BigQuery) for ML pipelines.
• Using managed ML pipelines and AutoML to simplify model development.

Case Study:

In 2025, Tech Mahindra announced the development of a telecom AI model built on AWS cloud infrastructure. The new multi-modal network-operations large language model (using NVIDIA AI Enterprise) runs on AWS, leveraging services like Amazon EC2 and EKS. 

This case study shows a major Indian tech firm using AWS to train and deploy advanced AI workloads. By shifting to the cloud, Tech Mahindra can scale compute for massive network data and streamline collaboration in building its AI solution.

Elevate your cloud computing skills with upGrad’s Fundamentals of Cloud Computing Free Course. Through this course, you can explore key cloud concepts like storage, databases, networking, virtualization, containers, and architecture. It also lets you utilize platforms like AWS, GCP, and Azure in AI projects.

Also Read: AWS v/s Google v/s Azure: Who will win the Cloud War?

13. Model Evaluation and Tuning

To build reliable AI models, you need to test how well they perform and then make them better. Evaluation uses metrics like accuracy, precision, recall, and ROC-AUC, along with methods like k-fold cross-validation to check real-world performance. 

Tuning adjusts model settings like learning rate or tree depth to improve results, using tools like grid search, random search, or Bayesian optimization.

This step helps catch overfitting and improve accuracy. You’ll work with tools like Scikit-learn’s GridSearchCV, TensorBoard for monitoring, and experiment trackers like MLflow or Weights & Biases to fine-tune models effectively.

Relevant Applications:

• Comparing multiple classification algorithms using cross-validation and ROC-AUC.
• Tuning hyperparameters (learning rates, tree depths, etc.) to increase accuracy.
• Evaluating model bias/variance trade-offs to avoid overfitting or underfitting.
• Validation/test splits are used to ensure that a model will be generalized before deployment.

Case Study:

LTIMindtree (formerly Mindtree) applied advanced model tuning to improve a telecom chatbot. By using Intel’s SigOpt optimization platform for hyperparameter tuning, they achieved double-digit accuracy gains and 63% faster inference time.

In this case, conventional grid-search was too slow, so the team used SigOpt’s Bayesian optimization to explore configurations efficiently. The result was a more accurate, cost-effective chatbot model, illustrating how systematic tuning and evaluation can dramatically boost AI performance.

14. Data Preprocessing & Feature Engineering

To build effective machine learning models, you need clean, structured, and meaningful data. That’s where data preprocessing and feature engineering come in. This involves handling missing values, scaling features, encoding categorical data, and creating new features that reveal deeper patterns.

These skills are pertinent for AI engineers since model performance heavily depends on data quality. Common tools include Pandas and NumPy for data handling, Scikit-learn and TensorFlow Data Validation for pipelines, and FeatureTools or OpenRefine for advanced transformations.

Relevant Applications:

• Cleaning and normalizing transactional data to improve fraud-detection model accuracy.
• Applying dimensionality reduction (e.g., PCA) on sensor readings for anomaly detection.
• Encoding categorical attributes (one-hot, label encoding) for customer segmentation models.
• Generating aggregated or lagged features (e.g., rolling averages) for time-series prediction.

Example:

In practice, the impact of data quality is profound. Industry experts note that even the most sophisticated algorithms cannot improve business decisions if the data isn’t clean and of good quality. For example, an Indian logistics firm recently overhauled its dispatch and delivery data by filling gaps and standardizing formats. 
This meticulous preprocessing step improved predicting delivery times and optimizing routes, demonstrating how feature engineering can directly boost real-world model accuracy.

Also Read: Data Preprocessing in Machine Learning: 7 Key Steps to Follow, Strategies, & Applications

15. Model Deployment & MLOps

Model deployment and MLOps focus on automating the release, monitoring, and scaling of machine learning models in production. This involves containerizing models with Docker, managing workflows with Kubernetes, and setting up CI/CD pipelines for continuous updates. 
Tools like MLflow and Kubeflow streamline tracking and deployment, while cloud platforms like AWS SageMaker and Google AI Platform support end-to-end model operations. Automation tools like Jenkins and GitHub Actions help maintain reliable and efficient ML pipelines.

Relevant Applications:

• Automating the deployment of a chatbot model behind a REST API for customer support.
• Continuously retrain a sales forecast model as new data arrives to keep predictions up-to-date.
• Monitoring model performance (drift, accuracy) and triggering alerts if it degrades in real time.
• Scaling image-recognition services using Kubernetes to handle spikes in user requests.

Case Study:

In India, many AI pilots struggle to reach production. NASSCOM reports that only about 27% of ML projects successfully move to deployment. To bridge this gap, companies are adopting MLOps best practices. Financial firms use CI/CD and containers to deploy fraud-detection models, while retailers use A/B testing for recommendation engines.

NASSCOM highlights that MLOps automates ML model development and ensures automated and reliable ML model deployment, consistent training, and monitoring. By integrating these practices, Indian businesses have achieved faster, more reliable model releases and greater ROI on AI initiatives.

16. Knowledge of APIs and Frameworks (TensorFlow, PyTorch)

Frameworks like TensorFlow and PyTorch make it easier to build, train, and deploy AI models without starting from scratch. They offer ready-to-use layers, optimizers, and tools for deep learning. TensorFlow (with Keras) and PyTorch are the go-to choices for most AI engineers today. 

You’ll also find Scikit-learn helpful for traditional ML, Hugging Face Transformers for NLP, and OpenCV for computer vision. These tools come with model libraries and utilities that speed up development, testing, and deployment.

Relevant Applications:

• Using TensorFlow to build and train convolutional neural networks for image classification.
• Employing PyTorch to prototype custom neural architectures for research projects.
• Leveraging the Hugging Face Transformers API to fine-tune pre-trained language models on new text data.
• Applying Keras high-level API to assemble and train neural nets for tabular data quickly.

Case Study:

A recent Indian collaboration illustrates the power of open AI frameworks. In early 2025, Hugging Face partnered with IISc/ARTPARK to improve access to Vaani, India’s largest multi-lingual dataset, encouraging AI developers to build language models for regional Indian languages.

This partnership uses Hugging Face’s PyTorch-based APIs and open-source tools, enabling researchers to train models to understand India’s linguistic diversity. These frameworks let teams skip infrastructure setup and focus on innovation, speeding up AI development in education and local language tech.

17. Ethics in AI Development

Ethical AI development means building systems that are fair, transparent, accountable, and respectful of user privacy. It’s important to understand concepts like bias in data, model explainability, and informed user consent, especially when deploying AI at scale. Tools like IBM’s AI Fairness 360, Microsoft’s Fairlearn, and InterpretML help detect bias and improve transparency. 

For privacy, libraries like PySyft and TensorFlow Privacy support secure data handling. Following frameworks like NITI Aayog’s AI ethics guidelines and data protection laws ensures your AI systems are not just effective, but responsible too.

Relevant Applications:

• Auditing a recruitment AI system to ensure it does not unfairly screen out applicants by gender or caste.
• Implementing explainable AI techniques (e.g. , SHAP values) to clarify medical diagnosis predictions for doctors.
• Using anonymization or encryption to protect sensitive customer data used in training models.
• Building fairness checks into loan-approval models so they meet ethical lending standards.

Example:

Ethical AI challenges have real consequences. For instance, a hiring platform found its resume-screening AI biased against certain demographic groups. The company tackled this by cleaning and diversifying its training data and incorporating fairness metrics into its pipeline. 

It also established governance policies for model use. As a result, the bias was reduced and the system became more transparent and trustworthy. This example reflects on the importance of actively addressing ethical concerns on grounds of fairness and accountability in AI projects.

18. SQL and NoSQL Databases

AI systems rely on both SQL and NoSQL databases to manage different types of data. SQL tools like MySQL and PostgreSQL work well for structured, transactional data with fixed schemas. On the other hand, NoSQL options like MongoDB or Cassandra handle unstructured or large-scale data with more flexibility. 

This is why knowing when to use each is pertinent. SQL is great for clean, fast analytics, while NoSQL is ideal for logs, time-series, or document-based data. Common tools include PostgreSQL, AWS Aurora, MongoDB, Redis, and big data platforms like Hadoop and Spark.

Relevant Applications:

• Storing structured customer profiles in a SQL database for quick join queries and reporting.
• MongoDB (NoSQL) holds large volumes of user-generated text and media for an AI recommendation system.
• Implementing Redis (NoSQL key-value store) to cache real-time model prediction results for low latency.
• Leveraging time-series databases (e.g., InfluxDB) to manage high-frequency sensor data for forecasting models.

Case Study:

India’s Aadhaar system is a strong example of NoSQL at scale. The UIDAI uses MongoDB to store biometric and demographic data for over 1.2 billion citizens. Its document-based structure handles large, fast-growing datasets more efficiently than traditional SQL systems.  

This massive, production-grade NoSQL deployment highlights how AI engineers rely on database choice. AI engineers commonly use SQL for handling structured, transactional data. For large-scale, diverse datasets, such as those in national identity systems, they turn to NoSQL databases like MongoDB.

Want to master both SQL and NoSQL for AI applications? upGrad’s Post Graduate Certificate in Data Science & AI. Learn how SQL and NoSQL databases like PostgreSQL and MongoDB support structured analytics. They also handle large-scale unstructured data, crucial for building reliable AI systems.

19. Data Annotation and Labeling Techniques

Data annotation (labeling) is tagging data (images, text, audio, etc.) with meaningful labels that supervised AI models can learn from. It's a key but often overlooked skill for AI engineers, crucial in supervised learning. It includes drawing bounding boxes in images, transcribing audio, or tagging sentiment in text. 
Annotation can be done manually (with tools like LabelImg or VIA), via crowdsourcing platforms, or with semi-automated tools (e.g., Labelbox, Amazon SageMaker Ground Truth). Maintaining annotation guidelines and quality checks (expert review) is necessary to ensure consistency.

Relevant Applications:

• Labeling medical scans (e.g., marking tumors in X-ray images) to train diagnostic AI models.
• Annotating video frames with object classes (person, vehicle, etc.) for training surveillance AI.
• Tagging customer feedback as positive, negative, or neutral sentiment for NLP models.
• Creating speech transcripts from audio recordings to build voice recognition datasets.

Case Study:

Building high-quality datasets is a priority in India’s healthcare AI. The MIDAS platform (ICMR/IISc/ARTPARK initiative) is creating annotated medical imaging datasets for AI-driven healthcare. 

Using a human-in-the-loop approach, radiologists and AI systems jointly annotate images. Initial labels are generated automatically, then experts review and correct them. This semi-automated process helps accurately label India-specific data like X-rays and pathology slides, supporting AI tools tailored to local healthcare needs.

Also Read: 16+ Best Data Annotation Tools for 2025: Features, Benefits, and More

20. Time Series Forecasting

Time series forecasting is all about predicting future outcomes by learning from past data patterns, like stock prices, weather, or sensor readings. If you're just starting out, it's helpful to explore models like ARIMA, Facebook Prophet, or neural networks such as LSTM and GRU. 

Python libraries like Statsmodels and Prophet make it easier to get started, while tools like TensorFlow or PyTorch are great for building deep learning models. This skill is especially useful in sectors like finance, retail, and IoT, where knowing what comes next can drive smarter decisions.

Relevant Applications:

• Forecast sales volumes over the next quarters to manage retail inventory.
• Predicting electricity demand or renewable generation for grid management.
• Modeling stock prices or economic indicators for financial analysis.
• Anticipating traffic flow or network load to optimize infrastructure usage.

Example: 

AI-driven forecasting is already improving energy management in India. For instance, in one pilot project in Gujarat, machine learning models were applied to predict solar power generation from weather data. 
Using historical weather patterns and solar output, the AI model reduced the error gap between expected and actual generation by about 30%. This accurate solar forecasting helped grid operators balance supply and demand more efficiently, lowering costs and reducing reliance on costly backup fuel sources.

Develop forecasting models with upGrad’s Post Graduate Programme in Machine Learning & AI. Learn to analyze time series data, enabling accurate predictions for various applications.

21. Version Control (Git)

Version control systems like Git are essential for managing changes in code, data, and models across AI projects. They allow you to track modifications, branch off experiments, and safely revert to earlier versions when needed. Tools like GitHub and GitLab make collaboration seamless by hosting repositories and enabling team-based workflows. 

Git also integrates with CI/CD pipelines, automating testing and deployment. This helps ensure reproducibility, maintain clean project history, and prevent accidental data or model loss.

Relevant Applications:

• Developers use Git to review each other’s notebooks and merge improvements.
• Data scientists create branches to test new models without affecting the main project.
• Tagging releases of models or datasets to mark stable versions for deployment.
• Checking out earlier commits to undo errors or compare different model versions quickly.

Case Study:

GitHub’s 2024 Octoverse report noted India had over 17 million developers on GitHub, making it the fastest-growing Git user base globally. This scale shows how common Git workflows have become in India’s tech scene. 

Using Git’s branching and merging, teams can experiment safely and integrate improvements, reducing errors and accelerating AI feature releases. Git-based workflows help Indian developers collaborate better and speed up AI project delivery, boosting overall productivity.
Enhance your collaboration skills with upGrad’s Professional Certificate Program in Cloud Computing and DevOps. This course teaches version control using Git, facilitating efficient teamwork in AI projects.

22. Prompt Engineering

Prompt engineering is the art of crafting and refining input prompts to guide large language models (LLMs) like ChatGPT toward desired outputs. Well-designed prompts unlock LLMs’ potential by steering responses with context and examples. 

Good prompt design boosts output accuracy without retraining the model. Tools like LangChain help automate prompt chaining for complex workflows. You can refine prompts interactively using ChatGPT, OpenAI API, or Google Vertex AI for faster iteration and better results.

Relevant Applications:

• Asking an LLM to condense long articles or reports into concise summaries.
• Designing prompt flows that maintain context and tone in a customer support chatbot.
• Prompting an AI model to write Python code or SQL queries from a natural language description.
• Using prompts to have the model generate marketing copy, stories, or translated text with specified style or keywords.

Case Study:

Bengaluru’s Presentations.ai launched an AI-driven platform for generating slide decks; by early 2025, it had over 5 million users. The startup reached 1M users in 3 months and earned millions of dollars in profit. 

Team members credit carefully designed prompts that tailor content to each user’s needs, effectively automating content creation at scale. This example shows how prompt engineering can drive product adoption and revenue growth directly.

Learn the art of crafting effective prompts with upGrad’s Advanced Prompt Engineering Course with ChatGPT. This course teaches advanced techniques to guide large language models toward desired outputs, improving the accuracy and relevance of AI-generated content.

23. Automation with AI Tools

Automation with AI tools means using intelligent software to reduce manual work in AI projects. For AI engineers, this includes platforms that automate data preprocessing, model training, and deployment. 

Standard tools include AutoML services (e.g., Google AutoML, H2O.ai) and workflow orchestrators (Apache Airflow, Kubeflow), as well as AI coding assistants (like GitHub Copilot). Automation boosts efficiency by running pipelines on schedule and generating code snippets automatically. 

Relevant Applications:

• Setting up Airflow or Kubeflow to automatically preprocess incoming data and train models on a schedule.
• An AutoML service tests many algorithms and hyperparameters and selects the best model without manual iteration.
• Employing tools like Copilot to auto-generate boilerplate code, unit tests, or documentation from comments.
• Implementing continuous integration (e.g., GitHub Actions) to retrain and deploy models when code or data changes automatically.

Case Study:

Freshworks (2024) reported that integrating AI coding assistants led to a 30% reduction in coding time and a 61% improvement in code quality. Engineers could focus on higher-value work by automating routine tasks like generating boilerplate code, tests, and documentation. 

The company cited these metrics publicly to demonstrate tangible productivity gains from AI-driven automation, making it a strategic priority. AI automation delivered a measurable impact on their development workflow as well.

24. Collaboration with Data and Product Teams

Effective AI engineers collaborate closely with data scientists, engineers, and product managers. This skill involves communicating insights, understanding product requirements, and aligning ML solutions with business goals. 

As an AI engineer, it’s important to translate technical concepts into user-focused terms. Strong collaboration helps ensure AI projects meet real user needs, avoid costly rework, and speed up deployment.
Relevant Applications:

• Working with product managers to translate business objectives into data features and model targets.
• Partnering with data engineers to ensure pipelines feed the correct data for ML training.
• Creating dashboards or presentations that explain model results and their impact on executives and designers.
• Using sprint planning and standups to sync on progress, integrate feedback, and adjust ML project timelines.

Case Study:

In 2025, Apollo Hospitals partnered with Microsoft to co-develop AI solutions for healthcare, focusing on disease progression and genomics. This collaboration united medical experts, data scientists, and engineers to build AI-driven products, aligning clinical needs with technical development. 

By working together across domains, the teams aimed to improve diagnostic tools and patient outcomes. The joint development of AI models for treatment planning shows how cross-team collaboration can integrate specialized knowledge into practical AI systems.

If you want to improve collaboration between technical and business teams, explore upGrad’s Post Graduate Certificate in Data Science & AI. This program aligns data scientists with product managers and decision-makers to successfully plan, build, and launch AI-driven solutions.

25. Model Interpretability and Bias Testing

Model interpretability refers to understanding how an AI model makes decisions, and bias testing ensures the model is fair across different groups. Explainable AI (XAI) techniques help engineers see which inputs influenced each prediction.

This transparency is crucial in sectors like finance and healthcare to maintain trust and accountability. Bias testing tools (e.g., IBM AIF360, Google What-If) measure performance across demographics to detect unfairness. Beginners should use these techniques to validate models, reveal hidden biases, and present results effectively.

Relevant Applications:

• Using SHAP to show which features (age, income, etc.) contributed most to a loan approval or denial.
• Computing false positive/negative rates for different demographic groups to check a model’s bias.
• Creating plots (decision trees, saliency maps) to illustrate how models split or weigh features.
• Examining training data distributions to spot underrepresented groups and correct imbalances before modeling.

Case Study:

In late 2024, India’s central bank formed a committee to build a Responsible and Ethical Enablement of AI framework, explicitly targeting algorithmic bias in finance. This initiative exemplifies India’s emphasis on bias testing in practice. 

Financial firms are now expected to implement transparency measures and fairness audits to ensure AI models treat all customer groups equitably. Such regulatory moves encourage banks to adopt explainable AI tools and bias-mitigation processes, improving trust in AI-driven services.

Now that you’ve seen the top AI skills in demand, let’s talk about how you can actually build and strengthen them practically and effectively.

How to Learn and Strengthen Your AI Engineer Skills?

AI engineering is about building scalable, ethical, and production-ready systems that solve high-impact challenges. As industries increasingly adopt AI, the demand for professionals who can apply algorithms effectively continues to grow.

To thrive in this field, you need more than academic knowledge. You must build practical expertise, stay fluent in emerging tools, and think critically about how your work fits into larger systems, especially as these AI skills in demand evolve.

The steps below will help you sharpen your skills, stand out in a competitive job market, and build lasting value in your career.

Step 1: Explore Online Courses and Certifications

Structured learning paths help you grasp key AI concepts step by step. They start with the basics and progress to advanced topics like machine learning, deep learning, and natural language processing. These certifications not only provide credibility but also help you stay focused on learning the core AI skills in demand that employers are actively hiring for.

How to do it:

• Learn from trusted platforms like upGrad, Google AI, and Microsoft Learn.
• Choose programs that match your goals. upGrad focuses on industry-level projects, and Google AI on TensorFlow.
• Aim for certifications like Google ML Engineer or DeepLearning.AI to boost credibility.
• Stick to a weekly study schedule with both theory and practice.

Step 02: Work on End-to-End AI Projects

Practical projects allow you to apply theoretical knowledge, understand the nuances of real-world data, and develop problem-solving skills. They also serve as tangible proof of your capabilities to potential employers.

How to do it:

• Use platforms like Kaggle, UCI, or Google Dataset Search to practice with real datasets. Start small, then tackle complex problems.
• Join open-source projects on GitHub to learn best practices and build your developer profile.
• Choose programs aligned with industry needs to build skills for AI that employers value in fast-paced roles.
• To strengthen your portfolio, create end-to-end projects covering data prep, modeling, and deployment.
• Take internships or freelance AI gigs on platforms like Upwork to gain experience.

Step 03: Build Fluency in AI Tools and Frameworks

Proficiency in these AI tools and frameworks is crucial to executing and optimizing your AI solutions efficiently. These tools form the core skills required for AI engineer success, enabling you to work efficiently and integrate seamlessly with professional AI teams. It also sharpens your command over key AI skills in demand, such as model building and experiment tracking.

How to do it:

• Practice building models using TensorFlow, PyTorch, and Keras. They’re core tools in deep learning.
• Use Pandas, NumPy, and Matplotlib for data prep and visualization in AI workflows.
• Master Jupyter Notebooks for fast prototyping and model testing.
• Learn MLflow to track experiments and manage model deployment efficiently.

Step 04: Learn Data Ethics and Responsible AI Practices

As an AI engineer, your work will impact many aspects of society. Being well-versed in ethical considerations, such as bias and transparency will ensure that your AI models are responsible and unbiased.

How to do it:

• Study ethical AI frameworks like IEEE’s guidelines to understand fairness and accountability.
• Learn to spot and mitigate biases using tools like IBM Fairness 360 and Google’s What-If Tool.
• Use SHAP and LIME to make your models explainable and transparent.
• Stay updated by following blogs, webinars, and research on responsible AI.
• Ethical awareness is integral to the skills for AI, ensuring models are fair, transparent, and socially responsible.

Step 05: Keep Up with AI Research and Tech Shifts

AI is one growing field, with new advancements and techniques emerging regularly. By keeping up with the latest research and innovations, you ensure that your skills remain relevant and you stay ahead of the curve in AI engineering.

How to do it:

• Follow platforms like Arxiv and attend conferences (NeurIPS, ICML, CVPR) to track new research.
• Read Google AI, OpenAI, and DeepMind updates to stay current with breakthroughs.
• Join communities on Reddit, AI Stack Exchange, and LinkedIn to discuss trends and papers.
• Explore advanced topics like GANs, Quantum ML, and Reinforcement Learning to deepen your skills.

If you're looking to advance your expertise and take the next step in your AI career, structured learning can make a measurable difference.

To strengthen your AI and machine learning foundation, explore upGrad’s Executive Diploma in Machine Learning and AI with IIIT-B. It’s a great way to gain expertise in core AI and ML concepts and prepare for real-world challenges.

Conclusion

To succeed in AI engineering, you need more than just technical know-how. You also need practical problem-solving abilities, ethical awareness, and fluency with industry tools. From Python and TensorFlow to NLP and MLOps, the top 25 AI skills in demand we’ve covered are what top companies look for when hiring AI talent.

Mastering the fundamentals, then building depth with real-world projects and collaborative tools. Staying updated with emerging trends and evolving frameworks will keep your expertise relevant.

That said, structured learning and expert mentorship can fast-track your journey.
upGrad’s programs, built in partnership with top institutions, are designed to help you confidently apply these skills.

Not sure where to begin? upGrad’s personalized career guidance can point you in the right direction, or visit upGrad’s nearest offline center to start learning today.

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