AI​/ML Engineer - Industrial Analytics

Company – TCS (MEA)

Job type – Full time

About Us:

Tata Consultancy Services (TCS) is an IT services, consulting and business solutions organization that has been partnering with many of the world’s largest businesses in their transformation journeys for over 50 years. TCS offers a consulting-led, cognitive powered, integrated portfolio of business, technology and engineering services and solutions. This is delivered through its unique Location Independent Agile™ delivery model, recognized as a benchmark of excellence in software development.

A part of the Tata group, India's largest multinational business group, TCS has over 616,171 of the world’s best-trained consultants with 157 nationalities in 53 countries. For more information, visit  and follow TCS news at @.

• Designed, developed, and deployed Machine Learning and Deep Learning models for industrial challenges involving large-scale numerical, categorical, and time series data, as well as images and videos.
• Extensive experience in Python programming for statistical analysis, custom algorithm development, and business insight generation using ML/DL models.
• Proficient in optimization techniques including Genetic Algorithms, Linear and Quadratic Programming, and others.
• Skilled in comprehensive data workflows: collection, exploratory analytics, data cleansing, feature selection, and model validation.
• Experience in deploying AI/ML solutions using MLOPs on cloud platforms such as Azure and AWS. Solid knowledge of NLP, Large Language Models, Retrieval-Augmented Generation, and ML algorithms such as Decision Trees, Clustering, Support Vector Machines, Artificial Neural Networks, LSTM, CNN, and YOLO, with awareness of their practical advantages and limitations.
• Motivated by continuous learning and mastery of emerging technologies and methodologies in the fields of artificial intelligence and machine learning.

• Experience in Oil & Gas, refinery, asset monitoring or other process industries.
• Exposure to GenAI, AI agents, RAG, knowledge graphs or hybrid physics-ML modelling.
• Knowledge of MLOps platforms, drift detection and responsible AI practices.