Technical Product Manager

We made history and now we work to transform the future - for our customers, our communities and our families. You'll see your work on the road every day, helping people move freely and pursue their dreams. At Ford, you can build more than vehicles. Come build what matters.

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 the 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.

As the Technical Product Manager for the Vehicle Electrical Software & Systems Engineering (VESSE) Ecosystem of AI Tools, you will lead the strategy and adoption of artificial intelligence solutions that accelerate the design, development, and validation of vehicle electrical and software systems.

In this position...

You are not just a manager of backlogs; you are a technical practitioner and internal evangelist. You will use low-code/no-code tools to rapidly prototype AI-driven engineering solutions-such as automated requirement analysis, synthetic data generation for testing, or predictive E/E architecture optimization. Your mission is to prove the value of AI to the VESSE community, drive the adoption of our existing tool portfolio, and build the business cases that scale these innovations across the global engineering organization.

Based in Dearborn, MI, this is a hybrid position with a required four-day onsite presence each week

As the Technical Product Manager for the Vehicle Electrical Software & Systems Engineering (VESSE) Ecosystem of AI Tools, you will lead the strategy and adoption of artificial intelligence solutions that accelerate the design, development, and validation of vehicle electrical and software systems.

What you'll do...

1. Ecosystem Strategy & Evangelism

* Portfolio Advocacy:
Act as the lead "product champion" for the existing VESSE AI tool suite. Drive high adoption rates among electrical, software, and systems engineers.

* Feature Discovery:
Work deeply within the VESSE engineering workflows to identify pain points; translate these into new features for the AI tool ecosystem.

* Community Building:
Lead workshops, "Ask Me Anything" (AMA) sessions, and internal demos to showcase how the AI ecosystem solves real-world engineering bottlenecks.

2. Rapid POC Development (Low-Code/No-Code)

* Hands-on Prototyping:
Use LCNC platforms (e.g., Streamlit, Github Copilot extensions for VS code, Agent mode using Claude, Chat GPT or Gemini models) to build functional "Quick-Win" POCs that demonstrate AI's potential in the VESSE space.

* Feasibility Testing:
Use these prototypes to validate technical feasibility and user desirability before committing full-scale engineering resources.

3. Business Case & KPI Ownership

* Value Quantification:
Build robust business cases for new AI tools and features. Link technical performance to VESSE-specific KPIs such as Engineering Hours Saved, Defect Detection Rate (DDR), Time-to-Market reduction, and System Reliability.

* ROI Tracking:
Monitor the impact of the AI tool ecosystem on the vehicle development lifecycle and report progress to executive leadership.

4. Technical Integration & Lifecycle Management

* Domain Expertise:
Ensure AI tools are deeply integrated with standard VESSE workflows (e.g., SysML, AUTOSAR, MBSE tools, and CI/CD pipelines).

* Data Strategy:
Partner with data engineers to ensure the VESSE ecosystem has access to high-quality, labeled engineering data for model training and fine-tuning.

What you'll do...

1. Ecosystem Strategy & Evangelism

* Portfolio Advocacy:
Act as the lead "product champion" for the existing VESSE AI tool suite. Drive high adoption rates among electrical, software, and systems engineers.

* Feature Discovery:
Work deeply within the VESSE engineering workflows to identify pain points; translate these into new features for the AI tool ecosystem.

* Community Building:
Lead workshops, "Ask Me Anything" (AMA) sessions, and internal demos to showcase how the AI ecosystem solves real-world engineering bottlenecks.

2. Rapid POC Development (Low-Code/No-Code)

* Hands-on Prototyping:
Use LCNC platforms (e.g., Streamlit, Github Copilot extensions for VS code, Agent mode using Claude, Chat GPT or Gemini models) to build functional "Quick-Win" POCs that demonstrate AI's potential in the VESSE space.

* Feasibility Testing:
Use these prototypes to validate technical feasibility and user desirability before committing full-scale engineering resources.

3. Business Case & KPI Ownership

* Value Quantification:
Build robust business cases for new AI tools and features. Link technical performance to VESSE-specific KPIs such as Engineering Hours Saved, Defect Detection Rate (DDR), Time-to-Market reduction, and System Reliability.

* ROI Tracking:
Monitor the impact of the AI tool ecosystem on the vehicle…