Solution Architect

Role Responsibilities: The role is that of a Solution Architect. In summary, that role is responsible for proposing technical, AI-based solutions for problems or opportunities already identified across PDT. More specifically, it has the following primary responsibilities: Analyzes an existing description of the problem or need where AI-based development can help, as for example from what is in a Product Requirements Document (PRD), and as needed, gathers additional understanding and extends the PRD, working in collaboration with the Product Manager. Creates a proposal of a high-level technical architecture (typically mainly software architecture, but there could still be other pieces, like manual steps) to best solve a problem previously defined, consisting of at least some AI components and any other needed components (front end, back end, infrastructure, data pipelines, new or refined neural network models, data, etc.) Works in collaboration with the Product Manager, a Project Manager, and technical implementation teams.

Basic Qualifications: A Bachelor's degree A minimum of 5 years designing and delivering production software systems; proven ability to define service boundaries, APIs, and scalable architectures with a minimum of 2 years developing or using ML, AI, or data driven solutions. Working knowledge of AI/ML system design, including data pipelines, deployment patterns, and operational monitoring. Strong data architecture fundamentals (batch/streaming, modeling, governance) and ability to derive data requirements from product needs. Experience with cloud infrastructure and deployment patterns; ability to design secure, observable systems. Excellent technical documentation skills: architecture diagrams, tradeoff analysis, and decision records.

Preferred Qualifications: In addition to the above required qualifications, any of the following are beneficial for this role. Experience architecting and delivering production LLM applications (RAG, vector search, tool calling/agents), including evaluation and guardrails. Hands-on MLOps/platform experience (CI/CD for ML, model registry, feature store, monitoring & drift detection). Experience in enterprise-scale, regulated environments and/or implementing responsible AI controls and auditability. Strong cost modeling skills for AI workloads (GPU serving, token cost optimization, capacity planning). A degree in an engineering field (e.g. computer, software, machine learning, intelligent systems, control systems, mechatronics, systems, mechanical engineering...) or a science field (e.g. physics, mathematics, ...) Ability to work across teams. Ability to build agreement. Experience working with global teams. 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.