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 :

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

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

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

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

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

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

- Integrate the AI solution with ERP and enterprise systems.

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

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

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

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

- Implement LLM evaluation, monitoring and performance optimization.

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

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

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

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

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

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

Required Technical Skills :

1. Generative AI / LLM :

- 8+ years of overall software/AI engineering experience.

- Strong hands-on experience with LLMs and Generative AI.

- Experience building production-grade GenAI applications.

- Strong understanding of LLM architecture, prompting and model selection.

- Experience with commercial and/or open-source LLMs.

- Strong experience with RAG architectures.

- Experience with embeddings and vector databases.

2. Agentic AI :

- Strong hands-on experience building Agentic AI solutions.

- Experience with Multi-Agent Orchestration.

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

- Experience designing agent workflows, state management and tool calling.

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

- Experience implementing agent guardrails and evaluation mechanisms.

3. Enterprise Integration :

- Experience integrating AI solutions with ERP systems and enterprise applications.

- Strong REST/API integration experience.

- Experience consuming external and real-time data sources.

- Experience building tool/API layers for AI agents.

- Understanding of enterprise security and access controls.

4. Machine Learning / Data Science :

- Strong foundation in classical Machine Learning.

- Strong understanding of statistics and statistical modelling.

- Experience integrating predictive/ML models with GenAI applications.

- Experience with Python and common ML libraries.

5. Cloud & Engineering :

- Strong Python programming skills.

- Strong software engineering and system-design fundamentals.

- Experience with AWS/cloud-based AI architectures.

- Experience with APIs, microservices and scalable backend systems.

- Understanding of CI/CD, containerization and production deployment.

Good to Have :

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

- Experience developing procurement analytics or category intelligence solutions.

- MCP / Model Context Protocol experience.

- Real-time event/data processing.

- AWS Bedrock or similar managed GenAI services.

- Kafka or other streaming technologies.

- LLM observability and evaluation platforms.

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

- 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:

- Basic ChatGPT/API integration experience.

- Simple RAG chatbot experience.

- Prompt engineering without software engineering.

- No Multi-Agent experience.

- No production GenAI implementation experience.

- No enterprise/API integration experience.

- Pure Data Science experience without hands-on GenAI engineering.