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Job details:
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
Good 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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