We are seeking a Agile Product Manager with approximately 10+ years of experience in the field. Owns the product outcome of an AI engagement — turning business goals into a prioritized, testable backlog, holding the line on what working means for a non-deterministic system, and driving adoption once it ships.
Responsibilities:
- Own the product backlog for AI engagements — outcome-framed epics, prioritized by value, feasibility and risk
- Translate business outcomes into AI use cases with explicit success criteria and acceptance thresholds
- Run discovery with client stakeholders and users to separate real problems from AI-shaped solutions
- Define what good enough means for a non-deterministic system, with AI Assurance and the client jointly
- Own the release plan across PoC, pilot and scale, including human-in-the-loop and fallback behaviour
- Prioritize data readiness and integration work alongside model work, not after it
- Run sprint ceremonies for the delivery pod and keep the team unblocked against a single set of priorities
- Manage scope, timeline and client expectations when model performance is uncertain
- Own adoption and change management — training, rollout comms, and measuring real usage
- Track value realization post-launch and feed it back into the roadmap
- Own responsible-AI and governance sign-off in the delivery flow, with Governance and Security
- Report progress, risk and outcomes to client and firm leadership
Requirements:
- 10+ yrs product management or product ownership, including 3+ yrs on AI/ML or data-intensive products
- Fluent in agile delivery (Scrum/Kanban/SAFe) and comfortable owning a backlog under real timeline pressure
- Understands the AI delivery lifecycle — data readiness, evaluation, deployment, monitoring, retraining
- Technically literate enough to challenge feasibility, cost and latency claims without designing the solution
- Comfortable writing acceptance criteria for probabilistic outputs — thresholds, guardrails, escalation paths
- Familiar with AI eval and observability practices well enough to act on the results
- Working knowledge of responsible-AI, privacy and governance obligations in regulated environments
- Strong discovery and facilitation skills — workshops, interviews, prioritization with senior stakeholders
- Data-literate on outcome measurement — instrumentation, adoption metrics, benefit tracking
- Clear communicator who can hold a client to a decision and say no to scope that will not deliver value
- Experience running change management and adoption for a system that alters how people work
- Success metrics: value realized against the business case · adoption/active usage post-launch · release predictability across PoC to scale · client-reported clarity of scope and priorities
