Role & 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

📌 Gen AI (Hyderabad)
🏢 Tata Consultancy Services
📍 Hyderabad