AI Product Manager

About the Role
We are seeking an AI Product Manager to spearhead the development and delivery of AI-driven products, solutions, and services from inception through implementation. This role requires collaboration with our clients, technical teams, and leadership to transform business needs into actionable product and delivery requirements, ensuring that the solutions we create provide measurable value.
This is a client-facing position, where you will be equally comfortable presenting to senior leadership as well as delving into technical details with engineers and data scientists. Your responsibilities will include shaping opportunities, guiding clients on realistic AI capabilities, translating business objectives into concrete requirements and deliverables, and building trust to foster long-term partnerships from successful projects.

Core Responsibilities

• Own the end-to-end product and delivery lifecycle for AI solutions, covering discovery and scoping through requirements definition, development, testing, client acceptance, launch, and iteration.
• Work directly with clients to define business and functional requirements, grasp their operational challenges, clarify priorities, and translate their needs into feasible solutions that align with their objectives.
• Lead the development of project requirements and Business Requirements Documents (BRDs), ensuring clarity on scope, use cases, workflows, assumptions, dependencies, acceptance criteria, and expected outcomes in collaboration with clients and internal teams.
• Convert business requirements into actionable product requirements, roadmaps, user stories, priorities, and delivery plans for engineering, data science, design, and other delivery teams.
• Oversee project delivery throughout the lifecycle, maintaining visibility on scope, timelines, dependencies, risks, decisions, and outstanding client inputs, collaborating closely with delivery teams to ensure project stays on track.
• Define and clarify delivery milestones and associated requirements, ensuring both internal teams and clients share a mutual understanding of expectations at each phase, what constitutes completion, and required inputs or approvals for progression.
• Manage the User Acceptance Testing (UAT) process from planning through final acceptance, including defining the UAT approach and acceptance criteria, preparing UAT scenarios and supporting documentation, coordinating client and internal testing activities, tracking issues and resolutions, and obtaining formal client approval and sign-off.
• Collaborate with technical teams to evaluate feasibility and make informed trade-offs in functionality, AI performance, user experience, delivery timelines, and technical constraints.
• Manage stakeholder expectations across multiple parties, maintaining clear communication, surfacing risks and decisions early, and keeping clients, technical teams, project stakeholders, and leadership aligned throughout delivery.
• Present product strategy, requirements, delivery progress, risks, milestones, and outcomes to senior and executive stakeholders on both internal and client sides.
• Support business development and solution shaping through leading client conversations, contributing to proposals and scopes of work, defining potential AI use cases, and showcasing capabilities.
• Stay informed about developments in AI and emerging technologies, identifying opportunities to enhance existing solutions or create new client value.

What We Value

• Strong communication and presence. Ability to simplify complex AI concepts, facilitate productive discussions around requirements, and communicate confidently with senior decision-makers.
• Requirements and delivery discipline. Capable of transforming vague business needs into structured requirements, maintaining clarity around scope and deliverables, and driving projects from discovery to client acceptance.
• Commercial instinct. Understanding of client business operations, ability to identify opportunities, and skill in presenting ideas without overselling technological capabilities.
• Stakeholder management. Competence in building trust, managing competing priorities thoughtfully, and aligning clients, leadership, and delivery teams.
• Technical depth. A sufficient understanding of AI and machine learning systems to challenge assumptions, assess feasibility, weigh trade-offs, and earn respect from technical teams.
• Ownership. Comfort in taking responsibility for outcomes, diligently following issues through to resolution, and ensuring commitments made to clients are understood and fulfilled.
• Experience. Approximately six years in product management, product delivery, business analysis, or related roles, including hands-on experience with AI, machine learning, or data-driven products.