Technical Product Manager

Ford Ford · Auto · Dearborn, MI +1 · Enterprise Technology

Technical Product Manager for Vehicle Electrical Software & Systems Engineering (VESSE) Ecosystem of AI Tools. Leads strategy and adoption of AI solutions to accelerate design, development, and validation of vehicle electrical and software systems. Uses low-code/no-code tools to prototype AI-driven engineering solutions, drives adoption of existing tools, and builds business cases for scaling innovations. Integrates AI tools with VESSE workflows and partners with data engineers for data access.

What you'd actually do

  1. 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.
  2. 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.
  3. 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.
  4. Ensure AI tools are deeply integrated with standard VESSE workflows (e.g., SysML, AUTOSAR, MBSE tools, and CI/CD pipelines).
  5. Partner with data engineers to ensure the VESSE ecosystem has access to high-quality, labeled engineering data for model training and fine-tuning.

Skills

Required

  • Bachelor of Computer Science, Data Science, Data Engineering or equivalent combination of relevant education and experience
  • 5+ years in a Technical Product Management or a "Solutions Architect" role preferably within the automotive or aerospace industry.
  • Data discovery; proven ability to identify, evaluate, and integrate high-value engineering datasets from across the VESSE lifecycle to power AI models.
  • Strong conceptual and practical understanding of Machine Learning (e.g., LLMs for requirements, Predictive Analytics for testing, or Computer Vision for hardware inspection).
  • Solid proficiency in the GCP (Google Cloud Platform) ecosystem, including the ability to set up projects, define IAM roles, manage service accounts, and handle API registrations to support secure and scalable AI tool development.
  • Expert-level proficiency in Agile methodologies and JIRA, with a deep understanding of the full SDLC as it applies to complex automotive software and systems engineering
  • Demonstrated ability to build functional prototypes using Low-Code/No-Code tools to solve engineering problems.
  • 5+ years of experience in Vehicle Electrical, Software, or Systems Engineering. You must understand the complexities of E/E architectures, ECU communication (CAN/Ethernet), and software-defined vehicle concepts.
  • Proven ability to lead and leverage diverse, cross-functional teams—including Data Engineers, Data Scientists, Full Stack/Front End Developers, and DevOps Engineers—across both US and offshore locations to deliver high-quality AI products on schedule.
  • Proven track record of "selling" technical products to internal stakeholders and driving cultural change within engineering teams.
  • Ability to develop financial models and business cases that justify the "Build vs. Buy" of AI tools.
  • Strategic Roadmapping

Nice to have

  • MBA or Master Degree of Computer Science, Data Science, Data Engineering or related field
  • Familiarity with Model-Based Systems Engineering (MBSE) and PLM tools.
  • Understanding of automotive safety and compliance standards (ISO 26262, ASPICE).
  • Experience with MLOps and the deployment of AI models into cloud-based engineering environments.

What the JD emphasized

  • technical practitioner
  • internal evangelist
  • low-code/no-code tools
  • rapidly prototype AI-driven engineering solutions
  • automated requirement analysis
  • synthetic data generation for testing
  • predictive E/E architecture optimization
  • prove the value of AI
  • drive the adoption
  • build the business cases
  • Data-Centric Mindset
  • AI/ML Literacy
  • Cloud & Infrastructure Proficiency
  • Agile & SDLC Mastery

Other signals

  • AI tools
  • low-code/no-code
  • prototype AI-driven engineering solutions
  • automated requirement analysis
  • synthetic data generation
  • predictive E/E architecture optimization
  • LLMs
  • predictive analytics
  • computer vision