About Ford & GDIA
At Ford Motor Company, we believe freedom of movement drives human progress. We also believe in providing you with the freedom to define and realize your dreams. With our incredible plans for the future of mobility, we have a wide variety of opportunities for you to accelerate your career potential as you help us define tomorrow's transportation. Creating the future of smart mobility requires the highly intelligent use of data, metrics, and analytics. That's where you can make an impact as part of our Global Data Insight & Analytics team. We are the trusted advisers that enable Ford to clearly see business conditions, customer needs, and the competitive landscape. With our support, key decision-makers can act in meaningful, positive ways. Join us and use your data expertise and analytical skills to drive evidence-based, timely decision-making.
About the Team
Industrial System Analytics (ISA) is a product group that develops cloud analytic solutions using GCP tools and techniques. This Technical Program Manager role is ideal for a technically fluent program leader who wants to drive delivery across a portfolio of strategic analytic solutions. You will collaborate with Business Customers, Product Management, Engineering, DP&E and IT to convert business goals into analytic solutions, working with Balanced Product Teams that embrace lean and agile practices, automated testing, and CI/CD.
This is a hybrid role suited for someone who can move fluidly between writing PRDs and prototyping a model, between running sprint planning and reviewing training data quality. You'll own the product vision, roadmap, and delivery for AI-driven features while staying technically credible with the engineering and data science teams you work alongside.
ResponsibilitiesKey Responsibilities
Product Management
- Define and own the product vision, strategy, and roadmap for AI/ML-powered products and features.
- Translate business problems and customer needs into clear product requirements, user stories, and acceptance criteria.
- Manage the end-to-end product lifecycle: discovery, prioritization, development, launch, and post-launch iteration.
- Run agile ceremonies (sprint planning, backlog grooming, retrospectives) and manage execution using Jira, Confluence, and similar tools.
- Balance and manage multiple concurrent product workstreams/portfolios, prioritizing across competing stakeholder demands.
- Define and track success metrics/KPIs (model performance, adoption, latency, cost-per-inference, business impact).
- Partner cross-functionally with engineering, data science, design, legal/compliance, and business stakeholders.
- Communicate roadmap, progress, and trade-offs clearly to leadership and stakeholders.
Hands-On Technical / AI-ML
- Directly contribute to prototyping, fine-tuning, evaluating, and iterating on LLMs and other ML models (e.g., prompt engineering, RAG pipelines, fine-tuning, embeddings).
- Collaborate closely with engineers/data scientists on data pipelines, model architecture trade-offs, and deployment strategy (MLOps).
- Evaluate build-vs-buy decisions for foundation models, vector databases, and AI infrastructure tooling.
- Stay current on the AI/LLM landscape (new model releases, techniques, regulatory/ethical considerations) and translate emerging capabilities into product opportunities.
- Bachelor of Engineering (B.E.) or B.Tech — Computer Science, Information Technology, Electronics, or related discipline.
- 5 to 7 years of total professional experience, including 3 years in Product Management (AI/ML or data-driven products preferred).
- Demonstrated hands-on experience building, fine-tuning, or deploying LLMs — not just managing engineers who do so (e.g., portfolio, GitHub, published work, or specific project examples).
- Practical knowledge of Python and common ML/AI frameworks (PyTorch, TensorFlow, Hugging Face) or LLM tooling (Google ADK, LangChain, LlamaIndex, vector DBs).
- Proven experience managing multiple products/workstreams simultaneously in a fast-paced environment.
- Proficiency with product/project management and collaboration tools: Jira, Confluence, or equivalents.
- Strong grasp of agile/scrum methodologies and experience running cross-functional agile teams.
- Excellent stakeholder management, written, and verbal communication skills — able to translate technical concepts for non-technical audiences and vice versa.
Preferred Qualifications
- Domain experience in automotive, mobility, or manufacturing industries is an added advantage.
- Solid understanding of NLP concepts, model evaluation metrics, prompt engineering, and RAG architectures is an added advantage.
- Familiarity with responsible AI practices — bias mitigation, model governance, data privacy, and compliance frameworks.
- Experience with cloud AI platforms (Azure ML, AWS SageMaker, GCP Vertex AI).
- Certifications in Product Management (CSPO, PMP) and/or AI/ML certifications are an added advantage.
What Success Looks Like in This Role
- Shipped AI/LLM-powered features that measurably improve user outcomes and business KPIs.
- A well-managed, transparent roadmap and backlog across multiple concurrent initiatives.
- Strong trust from both business stakeholders (for product judgment) and technical teams (for technical credibility).
- Continuous improvement of model quality metrics through direct hands-on involvement, not just delegation.
