Tech Lead - Vehicle Electrical Software Systems Engineering, Ecosystem of AI Tools

We are the movers of the world and the makers of the future. We get up every day, roll up our sleeves and build a better world -- together. At Ford, we’re all a part of something bigger than ourselves. Are you ready to change the way the world moves?

Enterprise Technology plays a critical part in shaping the future of mobility. If you’re looking for the chance to leverage advanced technology to redefine the transportation landscape, enhance customer experience and improve people’s lives, this is the opportunity for you. Join us and challenge your IT expertise and analytical skills to help create vehicles that are as smart as you are.

The VSSE- Ecosystem of AI Tools (VSSE-EAT) is at the forefront of Ford’s mission to integrate artificial intelligence into the heart of vehicle engineering. We are looking for a Technical Lead to lead a multidisciplinary team in transforming a fragmented collection of 0 to 1 stage AI projects into a robust, interconnected, and enterprise-scale ecosystem to drive quantified business value.

In this role, you will move beyond managing individual features. You will be the visionary architect of a platform that allows AI tools to collaborate using advanced frameworks such as Agent to Agent(A2A) or Model Context Protocol (MCP). You will empower both engineers and non-technical employees by building a low-code agent builder, ensuring that the power of AI is accessible across all of Ford Product Development.

Why this role?

This is a rare opportunity to set the standard for how a global automotive leader uses AI. You aren't just using tools; you are building the factory that creates them.
 

Key Responsibilities

1. Scaling & Standardization

• From POC to Production: Lead the re-architecting of tools currently in Discovery or POC stages to ensure they are compliant with Ford standards and can scale to support 1000s of global users.
• Robust Frameworks: Collaborate with your squad of Full-stack, Data and DevOps engineers to build an integrated and extensible framework that is both robust and efficient.
• Governance: Establish the "rules of the road" for AI tools within the ecosystem, ensuring they are secure, compliant, and maintainable.

2. Ecosystem Orchestration & MCP Leadership

• Multi-Agentic Strategy: Design the framework for a multi-agentic architecture where specialized agents (focused on hardware, electrical, or software) can collaborate seamlessly.
• MCP at Ford: Take the lead in implementing the enterprise standard for Model Context Protocol (MCP) in the VSSE ecosystem. You will define how different AI agents and tools share context and data to solve complex automotive problems in a secure and scalable way.
• Low-Code Empowerment: Drive the product roadmap for an Agent Builder that allows non-technical business customers to create and deploy AI agents, while maintaining the technical depth required for engineering teams.

3. Cross-Functional Leadership & Metrics

• Squad Leadership: Lead a dedicated team of Data Scientists, Product Designers, and Developers. You are the "glue" that connects user experience (UX) with deep data science and backend stability.
• Business Collaboration: Work closely with business customers to understand Ford-specific data (Hardware, Electrical, Software) to ensure data integrity and alignment.

• Bachelors degree in computer science, Engineering, or a related technical field, Masters degree preferred.
• 7+ years in software development
• 2+ years focused on Generative AI
• 3+ years of experience defining product vision, strategy, product roadmaps and building and managing backlogs
• 2+ years working with Agile software methodologies (Scrum, eXtreme Programming, Kanban)
• AI/ML Fundamental Literacy: Deep understanding of the LLM lifecycle, including fine-tuning, Retrieval-Augmented Generation (RAG), and the difference between probabilistic and deterministic outputs.
• Software Engineering Foundation: A background in professional software development (e.g., Python, Java, or C++). You must be able to discuss system architecture, API design, and latency trade-offs with your engineers.
• Architecting for Scale: Proven experience taking a software product from a local "lab" environment to a production environment supporting hundreds of concurrent users (knowledge of Kubernetes, cloud scaling, and load balancing).