LLM/Agentic AI Engineer

Job Title

Lead LLM / Agentic AI Engineer – AI-Led Procurement Transformation

Location: Kolkata
Experience: 8+ Years
Project Duration: Approximately 6 Months
Budget: Up to ₹3 Lakh/month
Work Mode: Onsite / Client-facing
Travel: May be required
Cloud: AWS preferred/important
Technology Focus: 50% Classical ML / Statistical Modelling + 50% LLM / GenAI

Role Overview

We are looking for a highly experienced Lead LLM / Agentic AI Engineer to drive an AI-led transformation initiative for the Procurement function.

The objective is to build a Procurement Category Insights Engine that combines classical Machine Learning, statistical modelling and Generative AI to generate actionable business insights.

The ideal candidate must have strong hands-on experience building production-grade LLM and Agentic AI solutions, including Multi-Agent Orchestration, real-time data integration, external data sources and enterprise/ERP integrations.

This is not a basic chatbot or RAG development role. The candidate will be expected to design and build a proper end-to-end Agentic AI solution capable of generating insights and triggering actionable outcomes through a simplified user experience.

Key Responsibilities

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  Lead the architecture and development of an enterprise-grade LLM/GenAI solution for procurement transformation.

  
  


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  Design and implement Agentic AI solutions capable of reasoning, planning and executing multi-step business tasks.

  
  


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  Build and orchestrate multiple AI agents to perform specialized procurement-related activities.

  
  


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  Design Multi-Agent workflows and determine when agents should interact with tools, APIs, data sources and enterprise systems.

  
  


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  Build a Procurement Category Insights Engine combining ML, statistical modelling and LLM capabilities.

  
  


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  Integrate real-time and external data sources to enrich procurement insights.

  
  


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  Integrate the AI solution with ERP and enterprise systems.

  
  


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  Develop tool/API integrations that allow AI agents to retrieve information and perform approved business actions.

  
  


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  Build actionable workflows where users can initiate business processes through a single-button / simplified action experience.

  
  


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  Develop RAG-based solutions where required for enterprise knowledge and contextual understanding.

  
  


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  Design prompt engineering strategies, agent instructions, evaluation mechanisms and AI guardrails.

  
  


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  Implement LLM evaluation, monitoring and performance optimization.

  
  


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  Collaborate with Data Scientists to incorporate classical ML and statistical models into the overall AI solution.

  
  


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  Work with backend and frontend engineers to deliver a complete end-to-end application.

  
  


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  Ensure solutions are scalable, secure, reliable and production-ready.

  
  


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  Rapidly adapt the architecture and solution as business problem statements evolve.

  
  


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  Lead technical discussions with client stakeholders and explain AI architecture and solution capabilities.

  
  


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  Support testing, production deployment, documentation and final client handover.

  
  

Required Technical SkillsGenerative AI / LLM

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  8+ years of overall software/AI engineering experience.

  
  


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  Strong hands-on experience with LLMs and Generative AI.

  
  


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  Experience building production-grade GenAI applications.

  
  


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  Strong understanding of LLM architecture, prompting and model selection.

  
  


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  Experience with commercial and/or open-source LLMs.

  
  


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  Strong experience with RAG architectures.

  
  


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  Experience with embeddings and vector databases.

  
  

Agentic AI

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  Strong hands-on experience building Agentic AI solutions.

  
  


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  Experience with Multi-Agent Orchestration.

  
  


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  Experience with frameworks such as LangGraph, LangChain, AutoGen, CrewAI or equivalent.

  
  


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  Experience designing agent workflows, state management and tool calling.

  
  


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  Ability to build agents that interact with APIs, databases and enterprise systems.

  
  


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  Experience implementing agent guardrails and evaluation mechanisms.

  
  

Enterprise Integration

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  Experience integrating AI solutions with ERP systems and enterprise applications.

  
  


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  Strong REST/API integration experience.

  
  


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  Experience consuming external and real-time data sources.

  
  


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  Experience building tool/API layers for AI agents.

  
  


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  Understanding of enterprise security and access controls.

  
  

Machine Learning / Data Science

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  Strong foundation in classical Machine Learning.

  
  


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  Strong understanding of statistics and statistical modelling.

  
  


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  Experience integrating predictive/ML models with GenAI applications.

  
  


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  Experience with Python and common ML libraries.

  
  

Cloud & Engineering

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  Strong Python programming skills.

  
  


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  Strong software engineering and system-design fundamentals.

  
  


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  Experience with AWS/cloud-based AI architectures.

  
  


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  Experience with APIs, microservices and scalable backend systems.

  
  


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  Understanding of CI/CD, containerization and production deployment.

  
  

Good to Have

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  Procurement / Source-to-Pay / Supply Chain domain experience.

  
  


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  Experience developing procurement analytics or category intelligence solutions.

  
  


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  MCP / Model Context Protocol experience.

  
  


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  Real-time event/data processing.

  
  


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  AWS Bedrock or similar managed GenAI services.

  
  


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  Kafka or other streaming technologies.

  
  


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  LLM observability and evaluation platforms.

  
  


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  Experience with enterprise ERP platforms such as SAP, Oracle or similar.

  
  


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  Experience working in consulting or client-facing transformation projects.

  
  

Ideal Candidate

The ideal candidate should be a hands-on senior AI engineer/architect, not simply a prompt engineer or RAG developer.

The candidate should be capable of:

Business Problem → AI Architecture → Multi-Agent Solution → Enterprise Integration → Production Deployment → Client Handover

Technology Balance

50% – Classical ML / Modelling / Statistical Analytics

50% – LLM / Generative AI / Agentic AI

The LLM/Agentic AI component is particularly important for this role.

Candidates NOT to Prioritize

Do not prioritize candidates who have only:

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  Basic ChatGPT/API integration experience.

  
  


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  Simple RAG chatbot experience.

  
  


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  Prompt engineering without software engineering.

  
  


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  No Multi-Agent experience.

  
  


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  No production GenAI implementation experience.

  
  


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  No enterprise/API integration experience.

  
  


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  Pure Data Science experience without hands-on GenAI engineering.