Senior Supply Chain AI Engineer

About the Role

We are looking for a Senior Supply Chain AI Engineer to develop AI solutions that support SCM forecasting, estimation, risk detection, RAG, and automated decision workflows. This role uses SCM data and business knowledge to predict future scenarios, identify risks, recommend actions, and trigger automated workflows.

The ideal candidate has hands-on experience applying machine learning and generative AI to real business processes and is comfortable owning a solution from problem framing through production deployment with minimal oversight.

Key Responsibilities

• Develop AI and machine learning models for SCM forecasting, estimation, and predictive analytics.

• Build solutions for demand forecasting, inventory risk, lead time prediction, material shortage detection, and supply risk analysis.

• Develop RAG-based solutions using trusted SCM knowledge, SOPs, documents, and business rules.

• Build automated prompt and AI-agent workflows to summarize insights, explain risks, and recommend actions.

• Create data-triggered automation logic for alerts, email notifications, workflow actions, and dashboard updates.

• Monitor and maintain deployed models and workflows in production, including performance tracking and handling failures or drift.

• Work with SCM users to translate business processes into AI models, prompts, and decision workflows.

• Integrate AI outputs with Microsoft Fabric, Power BI, internal portals, APIs, and automation platforms.

Requirements

• Degree in Computer Science, AI, Data Science, Mathematics, Statistics, Engineering, Software Engineering, or related fields or equivalent practical experience.

• 5+ years of experience in ML/AI engineering, including at least 2 years applying it to supply chain, operations, or manufacturing data.

• Strong Python programming skills.

• Experience with SQL and structured enterprise data.

• Familiarity with SCM processes such as planning, inventory, procurement, logistics, or warehouse operations.

• Working knowledge of RAG concepts, LLM integration, and prompt engineering - able to reason about retrieval design, grounding, and prompt structure even without production-scale deployment experience.

Good to Have

• Production experience building RAG solutions with enterprise documents, knowledge bases, or vector databases (e.g. chunking strategy, retrieval evaluation, embedding tuning).

• Experience building AI agents, automated prompt workflows, or multi-agent systems.

• Familiarity with agent communication and orchestration concepts (e.g. Agent-to-Agent (A2A), MCP, workflow orchestration, or agentic loop design such as ReAct-style iterative reasoning).

• Experience integrating LLMs through APIs and deploying AI-driven business workflows.

• Experience with Microsoft Fabric, Power BI, Power Automate, SharePoint, or other enterprise automation platforms.

• Familiarity with Azure or other cloud environments.