Senior AI Engineer

Inclusion without Exception

Tata Consultancy Services (TCS) is an equal opportunity employer, and embraces diversity in race, nationality, ethnicity, gender, age, physical ability, neurodiversity, and sexual orientation, to create a workforce that reflects the societies we operate in. Our continued commitment to Culture and Diversity is reflected in our people stories across our workforce and implemented through equitable workplace policies and processes.

Tata Consultancy Services (BSE: 532540, NSE: TCS) is the technology partner of choice for industry-leading organizations worldwide. Since its inception in 1968, TCS has upheld the highest standards of innovation, engineering excellence and customer service.

It has set an aspiration to become the world’s largest AI-led technology services company and is enabling its clients to transform themselves across the full AI stack, from infrastructure to intelligence.

Rooted in the heritage of the Tata Group, TCS is focused on creating long term value for its clients, its investors, its employees, and the community at large. With a highly skilled workforce spread across 56 countries and 194 service delivery centers across the world, the company has been recognized as a top employer in six continents. With the ability to rapidly apply and scale new technologies, the company has built long term partnerships with its clients. Many of these relationships have endured into decades and navigated every technology cycle, from mainframes in the 1970s to artificial intelligence today.

Job Description – Senior AI Engineer (Generative AI / Full‑Stack / Architecture)

Role Overview

The Senior AI Engineer will lead the design, development, and deployment of enterprise‑scale Generative AI solutions. This role involves ownership of system architecture, agentic workflow design, end‑to‑end GenAI pipelines, and high‑performance backend services. The ideal candidate combines deep engineering expertise with hands-on experience in LLMs, scalable microservices, and modern software development practices. This position also includes mentoring engineers and driving engineering standards across teams.

Key Responsibilities

Generative AI Engineering & Architecture

Lead the architecture, development, and production deployment of enterprise-grade Generative AI systems with a focus on scalability, reliability, and performance.

Design and implement agentic workflows using LLMs, tools, and orchestration frameworks to enable intelligent autonomous behaviors.

Build and integrate end-to-end GenAI pipelines, including model inference, RAG (Retrieval-Augmented Generation), embeddings, and application consumption via APIs.

Collaborate with data and platform engineering teams to implement vector search, semantic retrieval, and contextual grounding for AI‑driven responses.

Full-Stack & Backend Engineering

Apply strong full‑stack engi neering skills to build user-facing GenAI applications and robust backend systems.

Develop scalable microservices and secure RESTful APIs to expose AI and ML capabilities across enterprise applications.

Integrate GenAI functions with front-end interfaces built using modern web technologies.

Deployment, Operations, & Quality

Lead model deployment, packaging, performance optimization, and lifecycle management in production environments.

Ensure reliability and quality through test‑driven development (TDD), reusable component design, and modern architectural principles.

Drive CI/CD‑based releases, containerized deployments, and DevOps practices for AI-driven applications.

Collaboration & Leadership

Provide technical mentorship to intermediate engineers and contribute to architectural design reviews.

Champion engineering best practices, coding standards, and continuous improvement within Agile delivery teams.

Required Skills & Technical Expertise

Programming & Full‑Stack Development

Proficiency in Python, Java, R, and SQL.

Frontend and full‑stack development experience with ReactJS, HTML, CSS, and Node.js.

AI / ML & Generative AI

Hands-on experience with TensorFlow, Keras, and PyTorch.

Strong foundations in Generative AI, NLP, and NLU.

Expertise working with Large Language Models (LLMs) and prompt engineering.

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