Product Manager, Model Serving – AI​/ML Solutions Team

Product Manager, Model Serving – AI/ML Solutions Team

New York, NY, United States

You enjoy shaping the future of product innovation as a core leader, driving value for customers, guiding successful launches, and exceeding expectations. Join our dynamic AI/ML Solutions team and make a meaningful impact by delivering high-quality model serving infrastructure and capabilities that empower data scientists, engineers, and business stakeholders alike.

As a Product Manager in Model Serving, you are an integral part of the team that innovates new AI/ML product offerings and leads the end-to-end product life cycle. You will play a crucial role in developing and implementing our enterprise model serving platform capabilities spanning model deployment, inference infrastructure, and lifecycle management. Utilizing your deep understanding of how to get a product off the ground, you guide the successful launch of ML platform features, gather crucial feedback, and ensure top-tier client experiences.

With a strong commitment to scalability, resiliency, and stability, you collaborate closely with cross-functional teams including ML engineers, data scientists, and platform engineers to deliver high-quality products that exceed customer expectations.

• Develop a product strategy and product vision for model serving capabilities that delivers measurable value to internal and external customers across the AI/ML lifecycle
• Manage discovery efforts and market research to uncover model deployment and inference needs, integrating insights into a prioritized product roadmap
• Own, maintain, and develop a product backlog that enables development teams to support the overall strategic roadmap for model serving, including real-time and batch inference
• Build the framework and track key success metrics such as inference latency, throughput, model availability, and cost efficiency
• Lead end-to-end product delivery processes including intake, dependency management, release management, product operationalization, delivery feasibility decision-making, and product performance reporting, while escalating opportunities to improve efficiencies and functional coordination
• Carry out in-depth quantitative and qualitative analysis to support business cases for model serving investments and leadership decision-making
• Lead the completion of change management activities across functional partners and ensures adherence to the firm's risk, controls, compliance, and regulatory requirements including model risk governance standards
• Communicate proposed solutions and insights effectively to stakeholders across ML engineering, data science, risk, and business teams
• Promote adherence to industry standards and best practices for ML model deployment, serving infrastructure, and MLOps (e.g., model registries, CI/CD for ML, feature stores)
• Stay informed on industry trends and emerging technologies in model serving, LLM inference optimization, ML observability, and AI platform architecture

• 5+ years of experience or equivalent expertise in product management, with exposure to AI/ML platforms, MLOps, or a closely related domain
• Advanced knowledge of the product development life cycle, design, and data analytics, with specific familiarity with ML model lifecycle stages (training, validation, deployment, monitoring, retraining)
• Proven ability to lead product life cycle activities including discovery, ideation, strategic development, requirements definition, and value management
• Demonstrated ability to execute operational management and change readiness activities in a fast-moving AI/ML environment
• Strong understanding of delivery and a proven track record of implementing continuous improvement processes for ML platform capabilities
• Strong influencing and partnership/collaboration skills to drive cross-functional teams including data scientists, ML engineers, and platform architects to build better solutions and execute product go-live plans
• Experience in product or platform-wide release management, deployment processes, and strategies for ML systems; must be able to build solutions from the…