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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
Basic Qualifications:
- Bachelor's degree in Computer Science, Engineering, Data Science, Information Systems, or related technical field
- 8+ years building production software, AI/ML systems, or data platforms
- 5+ years architecting and building production AI/ML solutions in Python, including LLMs, RAG architectures, and vector databases, on cloud-native infrastructure (AWS or Azure), with at least 1+ years of hands-on experience in agentic AI frameworks (e.g., LangGraph, AutoGen, or equivalent)
- Experience with modern MLOps practices and CI/CD (GitHub Actions or equivalent)
- Proven ability to take AI or software systems from concept through production, including debugging, performance tuning, and operational support
- Comfortable operating independently and making architecture calls with incomplete information
- Strong communication skills - able to explain technical tradeoffs to engineers, product partners, and senior leaders
Preferred Qualifications:
- Experience with FastAPI or similar frameworks for building production AI/ML services
- Experience with PyTorch for model development, fine-tuning, or inference
- Experience with Snowflake and/or Databricks for data pipelines feeding AI/ML systems
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
At Stellantis, we assess candidates based on qualifications, merit, and business needs. We welcome applications from all people without regard to sex, age, ethnicity, nationality, religion, sexual orientation, disability, or any characteristic protected by law. We believe that diverse teams reflect our identity as a global company, enabling us to better address the evolving needs of our customers and care for our future.
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Stellantis
Aug 12, 2026