Lead AI Engineer - Agentic Supply Chain Systems

PortBlueSky is supporting a client that is building AI agent systems on top of a strong supply-chain data engineering pipeline. The Lead AI Engineer will design and implement agent workflows to improve forecasting, order management, and operational decision-making across supply-chain workflows.

Responsibilities:

• Build agentic supply-chain systems: Design, implement, and maintain agent workflows that support forecasting, order management, inventory planning, discrepancy detection, and operational recommendations
• Own agent orchestration: Build a central orchestration layer that routes tasks, coordinates multiple agents, manages tool and data-source access, and provides a clear user-facing interface
• Integrate with enterprise data systems: Connect agentic workflows to ERP data lakes, Lake House architectures, APIs, and existing AWS-hosted infrastructure
• Operate beyond prototypes: Ensure systems work on real production data and continue to perform under realistic scale, latency, reliability, and maintenance constraints
• Optimize production behavior: Monitor and improve token usage, agent routing, memory use, cost efficiency, scalability, and long-term sustainability
• Strengthen security: Implement prompt-security strategies, safe tool execution, access boundaries, and guardrails that prevent system hijacking, accidental destructive actions, or unsafe data loading patterns such as loading entire data sets into agent memory
• Use structured data contracts: Apply Pydantic models and validated input/output schemas so multi-agent systems remain predictable, typed, and auditable
• Build evaluation into the system: Create benchmarks, regression checks, reliability tests, and model-upgrade evaluations that track accuracy and behavior over time
• Lead through knowledge: Act as a senior IC, mentor, coach, and point of contact for agentic AI guidance across the project and occasionally across other teams
• Upskill the team: Help junior AI engineers and adjacent engineering teams understand agentic-system design, limitations, risks, and production practices