About the job
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.
Responsibilities
Develops a product strategy and product vision for model serving capabilities that delivers measurable value to internal and external customers across the AI/ML lifecycle
Manages discovery efforts and market research to uncover model deployment and inference needs, integrating insights into a prioritized product roadmap
Owns, maintains, and develops a product backlog that enables development teams to support the overall strategic roadmap for model serving, including real-time and batch inference
Builds the framework and tracks key success metrics such as inference latency, throughput, model availability, and cost efficiency,
Leads 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
Carries out in-depth quantitative and qualitative analysis to support business cases for model serving investments and leadership decision-making
Leads 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
Communicates proposed solutions and insights effectively to stakeholders across ML engineering, data science, risk, and business teams
Promotes 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)
Stays informed on industry trends and emerging technologies in model serving, LLM inference optimization, ML observability, and AI platform architecture
Qualifications
Minimum
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 ground up
Strong technical background with experience working on AWS, containerized workloads (e.g., Docker, Kubernetes), and model serving frameworks; experience with JIRA and Agile methodologies
Foundational understanding of ML model serving concepts including online vs. batch inference, model versioning, shadow deployments, and canary releases
Preferred
Demonstrated prior experience working in a highly matrixed, complex organization with multiple ML and data platform stakeholders
Practical experience with modern ML serving and orchestration technologies such as Ray Serve, Seldon, or Data Bricks
Experience with ML observability, model monitoring, and drift detection frameworks
Knowledge of LLM inference optimization techniques such as quantization, batching strategies, and GPU resource management
Familiarity with feature stores, model registries, and end-to-end MLOps pipeline