GEN AI

Greetings from Tata Consulting Services

TCS is Hiring for GEN AI

Experience : 5-9 years

Location: Hyderabad

Please find the JD below

About the Role

This is a Full Stack Software Development Engineer role within a team building customer- and workforce-facing

conversational AI solutions, responsible for building next-generation conversational AI, intelligent search, and

knowledge systems that power customer and associate experiences across multiple channels. This role requires

hands-on expertise in designing, developing, and productionizing scalable, secure, and enterprise-grade GenAI

solutions across cloud and on-premise environments.

Key Responsibilities

● Design and develop LLM-powered applications for conversational AI, knowledge retrieval, and insights delivery

● Build and orchestrate end-to-end GenAI pipelines, including prompt engineering, RAG, and agent-based workflows

● Develop and manage LLM agents (tool usage, API integration, function calling)

●

Integrate LLMs across cloud and on-prem/open-source environments

● Build RAG data pipelines (chunking, indexing, metadata enrichment)

●

Implement evaluation frameworks covering accuracy, hallucination, latency, safety, and validation (LLM-as-judge,

human-in-loop)

● Apply AI safety and guardrails, including prompt injection prevention and response validation

● Optimize models using prompt tuning, embeddings, and fine-tuning techniques

● Develop multimodal conversational experiences (text and voice)

● Build scalable backend services using microservices and REST APIs

● Enable observability (latency, token usage, cost, drift, quality monitoring)

● Contribute to POCs, experimentation, and adoption of emerging GenAI technologies

Required Skills

Core Engineering

● Strong proficiency in Java / J2EE, Spring Boot, RESTful APIs

● Strong proficiency in Python for AI/ML and GenAI workflows

● Experience in building distributed systems and microservices architectures

GenAI & LLM Expertise

● Hands-on experience with LLM frameworks (LangChain, LangGraph, Semantic Kernel) and RAG-based

knowledge retrieval systems

● Strong understanding of prompt engineering, workflow orchestration, and agent-based systems (tool usage,

function calling)

● Experience with vector databases, embeddings, and semantic search

● Experience working with LLMs across cloud platforms and on-prem/open-source models

● Experience designing LLM evaluation pipelines (benchmarking, prompt testing, LLM-as-judge)

● Strong understanding of hallucination mitigation, AI safety, guardrails, and prompt injection prevention

● Knowledge of PII handling and governance/compliance framework