Lead AI Engineer - Agentic Supply Chain Systems

Join Our Team as a Lead AI Engineer - Agentic Supply Chain Systems

Are you experienced in building production-grade agentic AI systems that operate reliably on real enterprise data? Do you understand the practical tradeoffs around orchestration, token efficiency, prompt security, evaluation, model upgrades, and long-term maintenance?

PortBlueSky is supporting a client that is building an AI agent systems on top of a strong supply-chain data engineering pipeline. The goal is to improve forecasting, order management, inventory planning, and operational decision-making across supply-chain workflows.

One example is a dashboard-based interface that monitors discrepancies between planned and actual supply across orders, shipments, and inventory, then suggests actionable options to the user.

This role is for someone who can build the central agent orchestration brain: the prime interface that distributes tasks, coordinates AI agents, connects to existing infrastructure, works with ERP-system data lakes and APIs, and keeps production behavior observable, secure, and reliable. The infrastructure is hosted on AWS.

Role and Responsibilities

As Lead AI Engineer, you will:

• 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.

What We Offer

• A serious production AI challenge: Work on agentic systems connected to real enterprise supply-chain data, not isolated demos.

• A strong technical foundation: Build on an existing data engineering pipeline and help shape the AI layer on top of it.

• High-impact ownership: Define how agents are orchestrated, evaluated, secured, monitored, and improved over time.

• A collaborative product team: Work with a Full Stack Engineer, Front-end Engineer, Designer, PM, and a growing engineering group.

• Leadership without leaving the code: Stay hands-on while mentoring and occasionally coordinating a small team of 2-3 junior AI engineers.

About You

You are an experienced AI engineer and technical leader with:

• Significant production experience building, running, and maintaining large agentic AI systems.

• Hands-on experience with Google ADK, LangChain, or LangGraph. Google ADK is preferred, and strong LangChain or LangGraph experience is also highly relevant.

• Strong Python skills. Node.js experience is acceptable where supported by deep agentic-system expertise.

• Strong understanding of Lake House architecture and enterprise data-platform integration.

• Knowledge of MCPs, data-source orchestration, tool access patterns, and safe interaction with external systems.

• Experience optimizing token usage and identifying inefficient or risky agent execution paths.

• Experience with prompt injection risks, prompt security strategies, permissions, guardrails, and production safety patterns.

• Experience using Pydantic or similar structured validation approaches for reliable agent input and output.

• Knowledge of evaluation approaches for agentic applications, including small benchmarks, regression testing, reliability checks, and model-upgrade tracking.

• Familiarity with AI coding agents such as Claude or Cursor, including their practical limitations and how to use them efficiently.

• Excellent communication skills and the ability to lead through expertise, mentoring, and clear technical guidance.

• Fluent English and comfort working in an international, remote-first team.

• EU residency and permission to work in the EU.

Nice to Have

• Experience with Microsoft Fabric or Databricks.

• Strong experience with Snowflake, AWS Redshift, or Google BigQuery.

• Familiarity with AWS Bedrock and Bedrock Agent Core evaluation features.

• Experience with supply-chain systems, forecasting, order management, inventory planning, or discrepancy-monitoring workflows.

• Experience designing dashboard-based AI interfaces for operations teams.

• Experience mentoring junior AI engineers or acting as a technical point of contact across multiple teams.

Technologies & Tools You May Work With

• Python

• Node.js

• Google ADK

• LangChain

• LangGraph

• MCP

• Pydantic

• AWS

• AWS Bedrock

• Lake House architectures

• Microsoft Fabric

• Databricks

• Snowflake

• AWS Redshift

• Google BigQuery

• ERP data lakes

• Supply-chain APIs

• AI coding agents such as Claude or Cursor

Ready to build agentic AI systems that make enterprise supply-chain decisions more reliable, transparent, and actionable? Apply now and help shape the production AI layer on top of a strong data foundation.

Apply Now!