You will build LLM features that run inside customers' ERP workflows: retrieval over their own documents, agents that draft and route transactions, extraction pipelines that replace manual data entry. The bar is production reliability — every feature ships with an evaluation set and a defined failure mode.
About the team
The AI & Data practice is the reason clients pick us over a conventional Odoo partner. We work alongside delivery pods rather than as an isolated R&D group, which means every model you ship has a named user inside a business process on day one.
What you'll do
- Design and ship RAG pipelines over customer documents — chunking, embedding, retrieval strategy, reranking, grounded citation.
- Build agentic workflows with LangGraph that read from and write to ERP systems safely, with human-in-the-loop checkpoints where the blast radius warrants it.
- Implement multi-LLM routing: pick the right model per task, control cost, and fail over cleanly.
- Build evaluation harnesses. A feature is not done until we can measure regressions on a fixed set.
- Handle the unglamorous 80%: document parsing, OCR quality, schema mapping, idempotency, retries and audit trails.
- Work directly with consultants and clients to define what "good enough to trust" means for each use case.
What we're looking for
- 4+ years of professional Python, with at least 1–2 years building LLM-backed systems that reached production.
- Hands-on LangGraph or LangChain experience — you know where the abstractions help and where they get in the way.
- Practical RAG experience: you've debugged bad retrieval, not just wired up a vector store.
- Comfortable with at least one vector database (pgvector, Qdrant, Weaviate, Pinecone) and honest about the trade-offs.
- You design for evaluation and observability from the start, and can explain a failure to a non-technical stakeholder.
- Overlap with either India, EU or US-East business hours.
Nice to have
- ERP or accounting domain knowledge — you understand why a wrong journal entry is not the same as a wrong chatbot reply.
- Fine-tuning or preference-optimisation experience.
- Document AI: OCR pipelines, table extraction, invoice/PO parsing at volume.
- Experience with self-hosted open-weight models and the cost maths behind that decision.
What we offer
Hiring process
- Screening (45 min) — a senior engineer on what you've shipped and what broke.
- Technical deep dive (90 min) — system design for a real RAG/agent problem, plus evaluation strategy.
- Founder conversation (45 min) — values, ownership, judgement under ambiguity.
Decision within 72 hours of the final round.
Not a perfect match on paper?Apply anyway. We've hired people who missed a listed requirement and were obviously right for the work — the CV is a starting point for a conversation, not a scorecard. Questions first? admin@iventureteam.com.