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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Platform Product Manager - **Company:** Ally Financial Inc. - **Location:** Charlotte, NC, United States (Remote available) - **Experience:** Experienced - **Salary:** $110,000.0 - $180,000.0 - **Contract:** Permanent contract - **Skills:** AI Evaluation, Application Programming Interfaces (APIs), Agile Methodology, Artificial Intelligence, Microsoft Azure, Information Engineering, Data Infrastructure, Programming Tools, Machine Learning, Platform as a Service (PAAS), Scrum Methodology, OpenAI, Azure Machine Learning, Data Streaming, Management of Software Versions, Retrieval-Augmented Generation, Model Validation, Agentic-AI, AI Platforms, Information Technology, Amazon Bedrock, Machine Learning Operations - **Published:** October 22, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=71516ada737be6a6 ## About the Role The ideal candidate has 10+ years of combined product management and AI/data engineering experience, including 5+ years managing enterprise platform products and 3+ years of hands-on ML or GenAI delivery experience. Strong knowledge of modern AI platforms, responsible AI, model governance, enterprise security, AI unit economics, Agile delivery, and regulated financial-services environments is required., * Bachelor of Science and 10+ years of combined experience across product management and hands-on ML, GenAI, or data/AI engineering, or equivalent combination of education and experience. Advanced degree in Computer Science, Engineering, Data Science, or related field preferred. * 5+ years of product management experience owning developer-facing, platform, or internal PaaS products (SDKs, APIs, gateways, model platforms, developer portals) with demonstrated ownership of versioned APIs, SLA contracts, and deprecation policies at enterprise scale. * 3+ years of hands-on practitioner background in ML, GenAI, or applied AI as an ML Engineer, Applied Scientist, Data Scientist, or Staff Engineer on an AI/ML platform. Must have shipped AI systems into production and be able to reason substantively about model evaluation, retrieval quality, prompt and agent design, drift, and inference cost. Pure non-technical product managers without a practitioner background will not be a fit. * Proven experience defining and delivering a GenAI and ML platform roadmap: model hosting and inference gateways, agent frameworks, retrieval-augmented generation, evaluation harnesses, prompt and model registries, and continuous monitoring for GenAI and classical ML workloads. * Familiarity with the modern AI stack - Amazon Bedrock, SageMaker, Azure OpenAI. Able to lead architectural trade-off discussions with senior engineers. * Understanding of the AI Development Life Cycle (AIDLC) across both classical ML (train, deploy, monitor, retrain) and GenAI/agent (prompt, retrieve, tool, evaluate, deploy, observe, iterate) flavors, and ability to map platform capabilities to each stage of the lifecycle. * Demonstrated ability to translate AI use cases into measurable business outcomes (cycle-time reduction, cost-to-serve, adoption of paved paths, data-quality uplift, operational efficiency) and to publish monthly platform scorecards visible to senior leadership. * Responsible AI capability: model governance, model risk management (MRM) partnership, human-in-the-loop controls for high-impact actions, auditability of AI recommendations, and continuous monitoring for drift, bias, hallucination, and prompt-injection risk. Comfortable operating alongside second-line functions in a regulated financial-services environment. * Strong Agile product leadership (Scrum/Kanban) with demonstrated ability to drive complex cross-team platform initiatives with predictable execution across multiple engineering pods, vendor partners, and governance stakeholders. * Excellent written and verbal communication: able to lead with a one-line verdict, back it with named contracts, decision rules, and anti-patterns, and present complex technical trade-offs clearly to both engineering and executive audiences. * Preferred: background in a bank, insurer, or health-payer AI platform organization. Prior contribution to or evaluation of model gateways, agent frameworks, or evaluation harnesses in production is a strong plus. Minimum qualifications * 7+ years of relevant experience * Bachelor's degree in relevant field of study or equivalent ## Description The Manager, Product Management - Enterprise Data & AI Platforms will define and deliver the product strategy and multi-quarter roadmap for Ally's shared GenAI, machine learning, data, and knowledge platform. This role owns developer-facing capabilities such as AI gateways, agent frameworks and templates, model and capability registries, evaluation tools, and reliability dashboards. The manager will translate business workload needs into reusable, secure, governed, and SLA-backed platform products; manage prioritization, versioning, adoption, reliability, and cost metrics; and partner closely with engineering, FinOps, Enterprise Architecture, Model Risk Management, and senior leadership., The Manager, Product Management, EDA Platforms, reports to the Head of the Data & AI Platform team and serves as a strategic partner in translating business priorities into a clear, actionable product roadmap. Working closely with the Head of Platform, this role ensures that platform investments and capabilities are aligned with enterprise needs, delivery-team demand, and measurable business outcomes. The role is responsible for clear articulation and tracking of product lines that powers Ally's GenAI and ML capabilities as a paved-path, self-service platform across Data Foundation, Knowledge Platform, Catalog & Governance, Model Platform, Apps & Agents, AI Reliability, and supporting Platform Fabric. The role owns key GenAI and ML product roadmaps used by delivery teams-including the AI Gateway, agent development tools and templates, capability and model-risk registries, and AI evaluation and monitoring dashboards-and partners with engineering and governance delivery leaders to prioritize, sequence, and evolve these capabilities in line with the Head of Platform's strategic direction. Day-to-day, the manager will define and publish a rolling multi-quarter roadmap sequenced by workload demand; own the versioning, SLA, deprecation, and paved-path contracts for each owned product; run the intake and prioritization loop that converts workload requests (predictive scoring, forecasting, document intelligence, conversational AI, agentic, streaming) into platform features, paved-path patterns, or explicit non-goals; partner with FinOps and Model Platform engineering on unit economics (cost-per-inference, GPU utilization, cost-per-agent-run) for GenAI and ML workloads; represent EDA Platforms in the EA and Model Risk Management forums with clear options, trade-offs, and recommendations; and instrument every product with adoption, reliability, and cost-to-serve telemetry so that platform health is visible to executives at all times. The manager will champion the platform mindset across the organization by systematically packaging recurring workload patterns into reusable products, making the paved path measurably cheaper and safer than bespoke alternatives, and holding the line against one-off exceptions that erode the platform contract.