Urgent Opening For AI Product Owner

Job Description

Profile : Product Owner AI/ML Position Overview We are seeking a forward-thinking Product Owner with 3+ years of experience to lead AI/ML product initiatives. This role demands a product-minded professional with a strong understanding of machine learning lifecycles, data-centric product development, and a proven ability to work cross-functionally. As a Product Owner, you will define the strategy and roadmap for intelligent solutions that solve real business problems and drive value through data and automation. Key Responsibilities 1. Market Research and Competitive Analysis Conduct in-depth market research to identify trends, user needs, and opportunities in the AI/ML space. Perform competitive analysis to benchmark against existing solutions and identify differentiation strategies. Use insights from market research to inform product strategy, feature prioritization, and go-to-market planning. 2. Product Vision and Strategy Define and communicate a clear product vision for AI/ML-powered solutions aligned with customer needs and business objectives. Develop and maintain a product roadmap that balances short-term delivery and long- term innovation. Partner with Data Scientists, ML Engineers, and Business Leaders to shape the product strategy for data-driven initiatives. 3. Discovery and Solutioning Drive product discovery using a data-first approach: analyze user behavior, explore market trends, and assess algorithm effectiveness. Lead problem-solving workshops and ideation sessions to uncover high-impact AI/ML use cases. Validate concepts through rapid prototyping and proof-of-value experiments. 4. Backlog Management Own and maintain a prioritized backlog focused on ML model development, data ingestion pipelines, and end-user features powered by AI. Collaborate with engineering and data science teams to break down technical tasks, clarify requirements, and ensure consistent delivery. Write detailed user stories, clearly articulating model behaviors, success metrics, and edge case handling. 5. Agile Project Execution Lead agile ceremonies including sprint planning, reviews, and retrospectives, ensuring alignment between business and technical teams. Coordinate across cross-functional teams (Data Science, Engineering, Design, QA) to deliver high-quality, production-ready AI features. Mitigate delivery risks by identifying dependencies, managing scope, and maintaining technical feasibility awareness. 6. Stakeholder Engagement Serve as the voice of the customer and business for all AI/ML initiatives, translating requirements into actionable work items. Present roadmaps, updates, and outcomes to internal stakeholders, ensuring transparency and alignment. Collaborate with compliance, legal, and ethical AI teams to ensure responsible deployment of intelligent systems. 7. Monitoring and Optimization Define KPIs and success metrics for AI features such as precision, recall, latency, user engagement, or automation rate. Collaborate with data teams to monitor model performance and user interaction to drive continuous improvement. Oversee A/B testing and post-launch analysis to validate hypotheses and identify optimization opportunities.