Lead Engineer

Who are we?

Tata Power is India’ s largest integrated power company with a 110 - year- old legacy committed to ‘Lighting Up Lives’ for generations to come. Our vision is to “Empower a billion lives through sustainable, affordable and innovative energy solutions”. Tata Power, together with its subsidiaries and joint entities, is present across the entire power value chain of conventional and renewable energy and next generation customer solution with future focus on innovation and technology, emphasis on renewable power, power distribution and service- led business. Tata Power has a domestic footprint with generation capacity of 15.7, GW (FY 25) from Thermal, Hydro, Waste Heat/ BFG, Wind and Solar energy, out of which 6.9 GW is from “Clean and Green sources” We have around 13 million customers and carry a clean energy portfolio of 44 %. With a bold aspiration to become the ‘Most Preferred Green Energy Company’, we are proactively investing in a greener portfolio and innovating with smart technology for a future - ready business. Tata Power has a holistic approach to Sustainability that covers environment, climate change, biodiversity, and community relations.

+ What are we looking for?

We are looking for a highly motivated AI/ML Engineer with 1–3 years of industry experience and a strong foundation in artificial intelligence, machine learning, and deep learning.

The ideal candidate will have hands-on experience in developing and deploying machine learning models and exposure to projects involving AI/ML, GenAI, Agentic AI, Digital Twins, Robotics, or advanced analytics.

This role is ideal for professionals passionate about solving real-world problems using AI/ML techniques and working on deep learning, image and video analytics, voice and text analytics, and cloud-based AI deployments.

• Education: Bachelor’s or Master’s degree in Computer Science, Computer Science Engineering, Data Science, AI/ML, or a related field.
• Experience: 1–3 years of hands-on experience in AI/ML model development and implementation, including experience working on real-world projects through professional roles or internships.
• Technical Expertise:

• Strong programming skills in Python.
• Experience with machine learning and deep learning frameworks such as TensorFlow, Keras, or PyTorch.
• Knowledge of data analysis and manipulation using NumPy and Pandas.
• Understanding of computer vision techniques such as YOLO, CNNs, and video analytics.
• Experience in developing models for image classification, object detection, NLP, and predictive analytics use cases.
• Hands-on experience in Exploratory Data Analysis (EDA) and basic statistical techniques.

• Frameworks & Tools:

• Familiarity with Scikit-learn and other machine learning libraries.
• Experience with cloud platforms such as AWS or Azure.

• Exposure to tools such as SageMaker, Azure ML, Databricks, or equivalent platforms.
• Understanding of model deployment, API integration, and microservices-based architectures.

• Additional Knowledge:

• Understanding of deep learning architectures such as CNNs, RNNs, and Transformers.
• Knowledge of NLP techniques including text classification, sentiment analysis, and language processing.
• Exposure to audio/voice analytics and signal processing techniques is an added advantage.

• Soft Skills:

• Strong analytical and problem-solving skills.
• Good communication and presentation abilities.
• High level of curiosity, ownership, and willingness to learn.
• Ability to work independently as well as collaboratively in a team environment.

+ You would be responsible for

• Design, develop, train, and evaluate machine learning and deep learning models.
• Develop algorithms for both structured and unstructured data.
• Participate in the end-to-end ML lifecycle, including data preparation, model development, testing, validation, and deployment.
• Build scalable models using frameworks such as TensorFlow, Keras, and PyTorch.
• Develop solutions for image classification, object detection, NLP, and predictive analytics use cases.
• Apply computer vision techniques such as YOLO, CNNs, and video analytics for detection, segmentation, and tracking.
• Design and implement computer vision pipelines for real-time and batch processing.
• Implement NLP solutions for sentiment analysis, text classification, topic modelling, and language understanding tasks.
• Work on audio and voice analytics, including feature extraction and signal processing.
• Perform Exploratory Data Analysis (EDA) using Python libraries and visualization tools.
• Clean, transform, and engineer datasets from multiple data sources.
• Develop and deploy ML models on cloud platforms such as AWS and Azure.
• Integrate models with applications using APIs, microservices, or lightweight deployment frameworks.
• Monitor model performance, conduct error analysis, and implement optimization techniques.
• Maintain proper documentation, experiment tracking, and version control for ML workflows.

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