Senior Artificial Intelligence Engineer

AI / GenAI Engineer & Lead LLMs & Agentic AI

Key Responsibilities – AI / GenAI Engineer (1–3 yrs)

• Build and finetune LLMbased applications: chatbots, copilots, document Q&A, and workflow assistants.
• Implement

  RAG pipelines

  using LangChain / LlamaIndex and vector databases (Pinecone, FAISS, etc.).
• Develop

  multistep/agentic workflows

  (task decomposition, tools, routing, memory) for real business processes.
• Integrate models via APIs/microservices (Python, FastAPI/Flask) and work with frontend/backend teams.
• Evaluate LLM apps using qualitative and quantitative metrics (latency, cost, hallucinations, accuracy).

Additional – Lead GenAI Engineer (5–8 yrs)

• Own endtoend

  LLM & agentic architecture

  : model/provider selection, context strategy, safety & security.
• Design reusable

  agent frameworks

  (orchestration, tools, observability) on AWS/Azure/GCP.
• Lead and mentor a small team; review prompts, flows, and code; enforce GenAI best practices and guardrails.
• Work directly with clients to shape the GenAI roadmap, PoCs, and production rollouts.

Required Skills

• Strong Python; solid understanding of LLMs, prompt engineering, and RAG concepts.
• Handson experience with at least one LLM stack (OpenAI/Anthropic/Bedrock/Gemini) and LangChain/LlamaIndex.
• Experience with Git and at least one cloud (AWS/Azure/GCP).

Good to Have

• Experience with tools/agents (AutoGen, CrewAI, Semantic Kernel, or similar) and MLOps/LLMOps tooling.