About us

At R Systems, we are shaping the future of technology by designing cutting-edge software products, platforms, and digital experiences that drive business growth for our clients.

Our product mindset and advanced engineering capabilities in Cloud, Data, AI, and Customer Experience empower us to deliver innovative solutions to key players across the high-tech industry. This includes ISVs, SaaS, and Internet companies, as well as leading organizations in telecom, media, healthcare, finance, and manufacturing.

We are Great Place to Work® Certified™ in 10 countries where we have a full time workforce - India, the USA, Canada, Poland, Romania, Moldova, Indonesia, Singapore, Malaysia, and Thailand. This means we are a dynamic, global team that values continuous learning, collaboration, and innovation. Join us and experience a workplace where your contributions are celebrated, and your growth, development, and well-being are at the heart of everything we do!!

Company Link: R Systems | Digital Product Engineering : 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.

📌 GenAI Engineering Lead (Python, FastAPI) || 6 - 9 Years |Pune
🏢 R Systems
📍 Pune