Lead AI Engineer
First seen by Alion on Aug 21, 2026.
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.
Skills
Machine Learning, Artificial Intelligence, Python, Generative AI, LLM, RAG, ERP, Statistical Modeling