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 productsProven experience translating complex technical requirements into clear user stories, acceptance criteria, and product specifications for engineering teamsStrong understanding of machine learning concepts, generative AI technologies (such as LLMs, transformers, or neural networks), and AI infrastructure componentsExperience with agile development methodologies and product management tools (such as Jira, Azure DevOps, or Linear) for backlog management and sprint planningDemonstrated ability to work cross-functionally with data scientists, engineers, and business stakeholders to deliver technical productsExcellent analytical and problem-solving skills with experience using data to drive product decisions and measure successAdditional 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 environmentsFamiliarity with data pipeline technologies and MLOps tools (such as MLflow, Kubeflow, or DataRobot) for model lifecycle management and automated workflowsPrevious experience in financial services, fintech, or regulated industries with understanding of compliance requirements and data governance frameworksStrong presentation and stakeholder management skills with ability to communicate AI product value propositions to both technical and non-technical audiencesSkills Required
Agile Development Methodologies, Transformers, Neural Networks, Jira, Google Cloud, Azure, Azure Devops, Aws