Programming and foundations
• Solid Python. Practical working use of at least one of TypeScript / Java / Go.
• Solid SQL and data modelling; comfortable with both relational and vector stores.
• Sound software engineering fundamentals — testing, version control, CI/CD, code review discipline.
GenAI core (must be hands-on, not conceptual)
• LLM application development — prompt design and prompt engineering as an engineering discipline,
structured output, context management, token/cost optimisation.
• RAG — chunking and indexing strategy, hybrid and semantic search, re-ranking, query rewriting, grounding
and citation, retrieval evaluation. Awareness of when RAG is the wrong answer.
• Agentic systems — tool use, planning and decomposition, multi-agent orchestration, state and memory
management, error recovery and retries, MCP or equivalent tool-integration standards.
• Evaluation and LLMOps — building eval harnesses, LLM-as-judge with its caveats, tracing and observability
(Langfuse, LangSmith, Arize or equivalent), regression testing on prompt and model changes, monitoring in




production.
• Model landscape — practical judgement across frontier and open models; multi-model routing; understanding
of the cost/quality/latency trade-off rather than brand loyalty.
• LLM safety — prompt injection and jailbreak mitigation, data exfiltration risk in tool-using agents, hallucination
mitigation patterns, guardrails and validation layers.
Frameworks and tooling
• LLM orchestration: LangGraph / LangChain / LlamaIndex / Semantic Kernel or equivalent — and the
judgement to know when a framework is unnecessary overhead.
• Vector / search: pgvector, FAISS, Pinecone, Weaviate, Azure AI Search, OpenSearch or similar.
• Cloud AI platforms: at least one of AWS Bedrock / Azure AI Foundry / Google Vertex AI at production depth.
• Containerisation and deployment: Docker, Kubernetes basics, serverless patterns.
• Data and pipelines: Pandas, Airflow / Databricks / equivalent workflow orchestra

📌 Gen AI Lead (Pune)
🏢 R Systems
📍 Pune