We are seeking an experienced AI Product / Project Management Lead to manage AI products and projects from opportunity identification through delivery, adoption, and continuous improvement. You will combine product thinking, project management, and technical understanding to translate business needs into practical AI solutions.
Responsibilities:
Define product vision, roadmaps, and project plans aligned with business objectives.
Identify and prioritize AI and automation opportunities based on user needs, business value, technical feasibility, and data readiness.
Translate business requirements into user stories, acceptance criteria, and prioritized backlogs.
Manage scope, schedules, dependencies, risks, and delivery commitments.
Partner with engineers and architects on solution design, enterprise integrations, cloud requirements, and technical tradeoffs.
Define success measures and coordinate AI evaluation, testing, and business acceptance.
Work with IT, security, and compliance teams to address data privacy, access controls, and responsible AI requirements.
Lead stakeholder communication, rollout planning, user training, and adoption.
Track product performance, user feedback, costs, and realized benefits to guide improvements.
Coordinate cross-functional delivery and resolve issues to maintain progress and accountability.
Education & Experience Recommended:
Degree in Engineering, Computer Science, Information Systems, Business Administration, or a related discipline, or equivalent practical experience.
Typically has 7–10 years of relevant experience in product management, project management, technology delivery, or business transformation.
Experience delivering AI-enabled products or enterprise automation solutions into production.
Working knowledge of programming concepts, APIs, cloud platforms, and application infrastructure; hands-on technical experience is an advantage.
Benefits: No benefits mentioned in the job posting.
Impact & Scope:
Drives AI products and projects across business functions, improving adoption, productivity, service quality, and operational performance.
Complexity:
Balances evolving AI capabilities, ambiguous requirements, technical dependencies, and competing priorities to deliver practical business outcomes.
