Title: AI Engineer — Agentic AI & Supply Chain Automation

Company: Nuvo AI (AI arm of Meril Life Sciences)

Location: Vapi, Gujarat (on-site)

Experience: 2–4 years

About the Role

We're building the next generation of AI-driven automation for Meril Life Sciences, one of India's largest medical device companies. You'll own the design and development of agentic AI systems that transform how our supply chain operates — demand forecasting, inventory optimization, supplier intelligence, procurement automation, and logistics.

This is not a wrap an LLM around a chatbot role. You'll design multi-agent systems that reason over enterprise data, coordinate with ERPs and vendor systems, and take real actions with real business impact.

What You'll Do

Design and build end-to-end agentic AI systems for supply chain use cases (forecasting agents, procurement agents, supplier-risk agents, inventory-optimization agents)

Architect multi-agent workflows using LangGraph, CrewAI, or AutoGen — pick the right tool for the problem, not the trendy one

Build production-grade APIs and services in FastAPI/Python that expose AI capabilities to internal teams

Own the full lifecycle: prompt engineering, retrieval design, evaluation, deployment, monitoring, iteration

Integrate with enterprise systems (ERP, WMS, supplier portals) and design robust tool-use patterns

Set up observability and evaluation pipelines using Langfuse,



Phoenix (Arize), or LangSmith

Work directly with supply chain domain experts to translate business problems into AI solutions

Must-Have

2–4 years of hands-on AI/ML engineering experience, with at least 1 year building LLM-based systems

Strong Python and FastAPI; comfortable designing REST APIs and async workflows

Production experience with at least one agentic framework: LangGraph, LangChain, CrewAI, or AutoGen

Solid grounding in RAG systems — chunking, embeddings, hybrid retrieval, re-ranking

Experience with PostgreSQL and at least one of: Redis, Cassandra, or Neo4j

Experience deploying LLM-based systems to production (any of vLLM, Triton, TensorRT, or cloud inference)

Familiarity with LLM observability tooling (Langfuse / Phoenix / LangSmith)

Ability to reason about latency, cost, and reliability trade-offs in LLM applications

Valuable to Have

Experience with supply chain, manufacturing, or enterprise B2B domains

Knowledge graph experience (Neo4j) for supplier/product relationships

Multi-agent orchestration in production (not just POCs)

Fine-tuning experience (LoRA, QLoRA) on domain-specific data

Familiarity with time-series forecasting (Prophet, neural forecasters) — supply chain forecasting is a core use case and Exposure to regulated environments (medical devices, pharma, healthcare)

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