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.

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.

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.

• 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).
• Protocol & Framework Knowledge
  :
  Ability to quickly master and implement emerging protocols like Model Context Protocol (MCP) and multi-agent orchestration frameworks (e.g., Lang Chain, Auto Gen, or CrewAI).
• Data Systems
  :
  Proficiency in navigating complex data environments, specifically how to integrate AI tools with enterprise data lakes while maintaining governance.
• Lifecycle Management
  :
  Experience managing the 'Maturity Pipeline' - moving products from Discovery/POC through Beta to a hardened Production state.
• Platform Thinking
  :
  Ability to move beyond 'single-feature' thinking to build an extensible ecosystem where tools share context and interact via a unified framework.
• Metric-Driven Leadership
  : A track record of optimizing Time to Market (TTM) and User Adoption. You should be able to demonstrate how you’ve used telemetry and user feedback to pivot or accelerate a product.
• Squad Leadership
  :
  Experience leading a diverse team of Data Scientists, Full-stack/Front-end Developers, and Designers. You must be able to speak all their 'languages.'

• Automotive Domain Expertise
  :
  Familiarity with Ford-specific data structures (Hardware signals, Electrical architecture, or Vehicle Software versions). Understanding the 'V-Model' or PD engineering cycles is a huge plus.
• Low-Code/No-Code Product Experience
  :
  Prior experience building or managing an 'Agent Builder' or a 'Self-Service' platform that empowers non-technical users to create their own workflows.
• Advanced AI Research Interest
  : A…