Product Owner – AI/ML

Job Description

We are seeking a Product Owner to lead the vision, prioritization, and execution of cutting-edge agentic AI initiatives. This role is focused on end-to-end product ownership — driving autonomous, intelligent AI systems from concept to delivery. The ideal candidate will have strong experience in managing AI/ML products and a clear understanding of how to create value through intelligent agents, LLM-based automation, and ML workflows. Key Responsibilities Define and communicate the product vision for agentic AI systems, enabling intelligent, autonomous behavior across business-critical workflows. Own and maintain a well-prioritized product backlog, translating strategic goals and stakeholder needs into actionable user stories and epics. Collaborate closely with ML engineers, data scientists, software developers, and data engineers to ensure delivery aligns with technical feasibility and business impact. Drive development of AI features involving multi-agent orchestration, self-improving systems, tool-use, planning, and decision-making capabilities. Act as the primary liaison with stakeholders, gathering feedback, defining KPIs, and iterating on features to ensure continuous product improvement. Partner with MLOps, data, and engineering teams to align on system architecture, deployment strategies, and compliance for scalable AI delivery. Monitor progress and product health using appropriate metrics (e.g., adoption, latency, reliability, model performance). Contribute to go-to-market planning, user adoption strategies, and internal enablement for AI-driven features. Stay updated on trends in agentic AI, LLM toolchains, open-source agent frameworks (LangChain, AutoGPT, LangGraph, etc.), and bring those insights into product strategy. Required Skills & Experience 5+ years of experience in product management or product ownership, preferably in AI/ML-driven software environments. Proven track record of delivering successful AI/ML or intelligent automation products, ideally involving LLM-based agents, decision engines, or workflow orchestration. Strong understanding of the AI/ML development lifecycle, including data ingestion, model training, validation, deployment, and monitoring. Familiarity with modern AI agent architectures (planning, memory, tool use), and platforms like LangChain, AutoGPT, CrewAI, LangGraph, or custom agent stacks. Experience working in Agile teams, coordinating across cross-functional roles (engineering, data science, UX, business). Strong analytical and communication skills — capable of making trade-offs, defining MVPs, and translating complex technical work into user and business value. Comfort working with tools like Jira, Confluence, or similar backlog management platforms.