R Systems•2h ago
LinkedIn
GenAI Engineering Lead
Pune City, Maharashtra, India
Full Time
Senior Level
Full Job Description
- Experience and Education:
- 6+ years of relevant technology experience overall and 2+ years hands-on with LLM-based systems in production.
- Demonstrated experience leading technical teams and mentoring engineers.
- BE / B.Tech / MCA / M.Tech, or equivalent demonstrated capability.
- Programming and foundations:
- Strong 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 orchestration.
- AI-assisted development tooling (Claude Code, Cursor, Copilot) used seriously as a productivity multiplier, not as a novelty.
Company
R Systems
RSI is a trusted software engineering and digital transformation services partner for global enterprises navigating technological change. We enable technology companies, SaaS platforms, and enterprise...
Pune City, Maharashtra, India
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