Technical Product Owner (Gen AI)

Responsibilities and Impact:

• Define and execute product strategy for core AI services, tools, and infrastructure to enable scalable generative AI capabilities across the organization
• Partner closely with data science teams to identify, prioritize, and translate technical requirements into actionable product roadmaps and detailed user stories for engineering delivery
• Drive the development of AI tooling ecosystem including model deployment platforms, data pipelines, and MLOps infrastructure with a product-first approach
• Collaborate with engineering teams to ensure seamless integration of AI services while maintaining high standards for performance, security, and reliability
• Lead cross-functional initiatives to establish AI governance frameworks, best practices, and standardized workflows for generative AI implementations
• Champion user experience and adoption by gathering feedback from internal stakeholders and continuously improving AI tools based on usage patterns and business outcomes

What We're Looking For:

Basic Required Qualifications:

• Bachelor's degree in Computer Science, Engineering, Data Science, or related technical field with 3+ years of product management experience in AI/ML or data-driven products
• Proven experience translating complex technical requirements into clear user stories, acceptance criteria, and product specifications for engineering teams
• Strong understanding of machine learning concepts, generative AI technologies (such as LLMs, transformers, or neural networks), and AI infrastructure components
• Experience with agile development methodologies and product management tools (such as Jira, Azure DevOps, or Linear) for backlog management and sprint planning
• Demonstrated ability to work cross-functionally with data scientists, engineers, and business stakeholders to deliver technical products
• Excellent analytical and problem-solving skills with experience using data to drive product decisions and measure success

Additional Preferred Qualifications:

• Experience with cloud platforms (such as AWS, Azure, or Google Cloud) and AI/ML services including model deployment, monitoring, and scaling in production environments
• Familiarity with data pipeline technologies and MLOps tools (such as MLflow, Kubeflow, or DataRobot) for model lifecycle management and automated workflows
• Previous experience in financial services, fintech, or regulated industries with understanding of compliance requirements and data governance frameworks
• Strong presentation and stakeholder management skills with ability to communicate AI product value propositions to both technical and non-technical audiences