Gen AI

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