Gen AI Lead

R Systems; Maharashtra; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

R Systems is seeking a seasoned GenAI architect to lead production-grade GenAI systems in Maharashtra, India. You will drive end-to-end LLM projects, implement RAG and multi-model routing, ensure robust evaluation, tracing, and observability, and mentor engineers across teams.

The role emphasizes hands-on development, architecture decisions, and delivering measurable improvements in accuracy, latency, and cost while maintaining safety and governance.

Qualifications

• 6+ years of technology experience overall.

Skills

Python

TypeScript

Java

Go

SQL

Data modelling

Testing

CI/CD

Education

BE / B.Tech / MCA / M.Tech

Tools

LangGraph

LangChain

LlamaIndex

Semantic Kernel

Docker

Kubernetes

Airflow

Databricks

Pandas

Langfuse

LangSmith

Arize

Pinecone

FAISS

Weaviate

OpenSearch

Job description

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.

4. Evidence We Look For

• A GenAI system they personally shipped to production - its architecture, what broke, and what they changed as a result.
• A concrete number: accuracy or quality improvement, cost per transaction reduced, latency brought down, manual effort eliminated.
• An evaluation strategy they designed - how they knew the system was actually working, and how they caught regressions.
• A time they argued against using an LLM for something, and what they recommended instead.
• Something they built for reuse that other teams actually adopted.

5. Good to Have

• Experience in a client-facing consulting, professional services, or Forward Deployed Engineer model.
• Classical ML background - model lifecycle, feature engineering, forecasting - as context, not as the core of the role.
• Contributions to open source, technical writing, conference speaking, or an active community presence in the AI space.
• Relevant certifications (cloud AI, Anthropic, or equivalent), treated as supporting evidence rather than a substitute for shipped work.

6. Experience and Education

• 6+ years of relevant technology experience overall (typically 6-14, but we will not screen out strong candidates on either side of that).
• 2+ years hands-on with LLM-based systems in production. We are deliberately not asking for more. Production LLM application development is roughly three years old as a discipline - anyone claiming a decade of it is describing something else. Depth and evidence here outweigh total years.
• Demonstrated experience leading technical teams and mentoring engineers.
• BE / B.Tech / MCA / M.Tech, or equivalent demonstrated capability. We will interview strong self-taught engineers