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AI Engineer LLM Systems (Clinical AI) Location: On-site New Delhi Full-time Immediate joiners preferred We're building OneRx a clinical decision-support platform that gives doctors evidence-grounded answers, trial matches, drug & pricing data, and regulatory information, every claim backed by a citation they can trust at the point of care. It's regulated medical software, so our bar for correctness isn't \"good enough for a demo\" a confidently wrong answer is a patient-safety problem, not just a quality miss. We're looking for a hands-on AI Engineer to own LLM systems end-to-end: agentic pipelines, the Python backend, retrieval and grounding, and critically the prompt and eval layer where clinical reliability is won or lost. Build-from-scratch, high-ownership role on a lean founding team, working directly with the founder on architecture and product. What you'll do - Design multi-agent / multi-step pipelines in LangGraph - Write production Python FastAPI services, REST APIs, background workers, streaming (SSE) paths - Build and harden RAG over messy real-world clinical sources - Own prompt engineering as a core discipline system prompts, few-shot, CoT, structured JSON/XML outputs designed, versioned, and tested - Build the eval harness that keeps it honest ground-truth sets, regression suites, shadow-mode before enforcement, accuracy scorecards - Drive down hallucination so every clinical claim is grounded including knowing when to refuse or block an answer rather than fill a gap - Route across providers for the right cost/latency/quality per task - Engineer for determinism and cost correct cache keys, token budgets, latency targets - Containerize and ship with Docker on Azure Must-haves - Strong prompt engineering hands-on command of system-prompt design, few-shot, CoT, structured outputs, prompt versioning, and an eval-driven workflow (promptfoo, LangSmith, or your own). This is the core of the role, not a bonus. - Strong, production-grade Python clean services, not notebooks - LangGraph / LangChain you've actually built agentic, multi-node pipelines - RAG in practice vector DBs (Pinecone / FAISS / Qdrant), retrieval, grounding, and getting citations right on imperfect sources - FastAPI + REST API development - LLM APIs Anthropic, OpenAI / Azure OpenAI, Hugging Face - Docker and containerized deployment Nice to have - vLLM or other serving frameworks; streaming (SSE/WebSockets) - Postgres / MongoDB / Redis in production - MLOps / observability tooling - Experience in a domain where being wrong has real consequences and outputs must be auditable (healthcare, fintech, legal) You'll fit if you - Have 24 years building real AI/ML products (an exceptional 1-year portfolio counts) - Can show shipped LLM systems agents, RAG, or assistants real users actually used, ideally on GitHub - Can show prompt craft: prompts, eval suites, or measurable quality gains - Design before you code, and care that the thing is correct, not just that it runs - Move fast from idea to working system and own ambiguity on a small team Why join - Build a high-impact clinical AI product from the ground up, where quality genuinely matters - Direct line to the founder on architecture and product strategy - Lean, execution-focused team; high ownership, quick iteration How to apply Email resume + GitHub/portfolio to Subject: AI Engineer Application [Your Name] Job Types: Full-time, Permanent Compensation: 30,000.00 - 40,000.00 per month Benefits: - Commuter assistance - Food provided - Health insurance - Internet reimbursement Application Question(s): - minimum 2 year as an AI developer Experience: - AI: 2 years (Required) - Machine learning: 1 year (Required) Work Location: In person .
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