Solution Architect

Team:

This is a role on the “PDT AI Solutions Program Team”. The team is responsible for spreading and managing efforts to apply AI-based technologies across the Product Development and Technology (PDT) organization.

Vision:

The vision for this team to transform the large PDT organization into one where, everybody, from engineers to executives, is daily using best relevant AI technology to move faster, do more, and delight customers, and where the AI is production-grade, at scale, trustworthy, and adapted to Stellantis people.

Mission:

To get to that vision, we set our mission as improving PDT efficiency, speed, inventiveness, and quality, as well as Stellantis customer satisfaction, by setting-up, maintaining, and ensuring delivery:

1) projects for shared tools and resources to allow PDT teams to build their own AI-based applications and processes, as well as

2) projects for from-the-scratch, turn-key AI-based applications and processes for PDT teams that don’t have the resources to build their own;

while taking advantage of latest AI tech adapted to our people.

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 minimum of a Bachelors degree
• 5 years designing and delivering production software systems; proven ability to define service boundaries, APIs, and scalable architectures.
• 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.