Principal AI Architect - Supply Chain
@ StellantisPrincipal AI Architect - Supply Chain
About the job
A company building AI-enabled supply chain solutions, focusing on designing, prototyping, and implementing AI systems, pipelines, and applications in a technical, collaborative environment.
Requirements
- Experience with AI models and pipelines
- Proficiency in Python and FastAPI
- Understanding of cloud platforms (AWS, Azure)
- Experience with data tools (Snowflake, Databricks)
- Ability to debug and review code
Qualifications
- Strong technical and problem-solving skills
- Experience leading architecture reviews
- Team mentorship abilities
- Hands-on experience in AI system design
Full job description
We're building an AI-enabled supply chain that senses, predicts, prescribes, and acts. As Principal AI Architect, you'll design and build enterprise-grade AI systems — from data pipelines and models to agents and applications — that run in production across our Supply Chain organization.
This is a hands-on technical role. You'll be in the architecture, in the code, and in the weeds of production systems. You'll design solutions, prototype approaches, write and review code, and unblock engineering teams building alongside you.
Responsibilities include but not limited to:
- Architect and help build AI, generative AI, and agentic AI solutions — from proof of concept through production — using Python, FastAPI, PyTorch, LangGraph, and AutoGen
- Design solution architecture across data pipelines (Snowflake, Databricks), models, agents, RAG pipelines, vector databases, APIs, and React-based applications on AWS and Azure
- Get hands-on with complex technical problems: debugging production issues, prototyping new approaches, and reviewing code and system design
- Lead architecture reviews and technical decision-making, weighing tradeoffs across performance, cost, scalability, and maintainability
- Use GitHub, GitHub Actions, and GitHub Copilot to build and ship faster — for your own work and across teams
- Partner directly with product, data engineering, AI engineering, and platform teams to solve real technical problems, not just review their plans
- Mentor engineers through pairing, code review, and hands-on problem-solving
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