AI Product Leader

Blank Metal•Minneapolis, MN

4d•Hybrid

About The Position

The AI Product Leader sits at the intersection of product delivery, engagement leadership, and applied AI strategy. In practice it looks like: ~40% product delivery, ~40% client leadership, ~20% AI strategy. You will lead 2–3 concurrent engagements and be accountable for shipping, adoption, and measurable impact.

Requirements

Shipped software to production and can point to what you built, what changed, and how it performed.
Can run delivery: budget, backlog, sprints, trade-offs, and stakeholder alignment (without hiding behind process).
Fluent in modern GenAI patterns (prompting, RAG basics, evaluation, iteration) and have hands-on experience using tools to build prototypes or workflows.
Thrive in ambiguity: starting with incomplete requirements, making decisions with imperfect information, and adapting as you learn.
Client leadership can be external clients or internal “enterprise clients” where you owned budget, outcomes, timelines, and executive stakeholders.
Experience with AI-powered low-code platforms (V0, Bolt, Lovable) for rapid prototyping and stakeholder demos
AI experience can be GenAI product work or adjacent data/ML product work plus clear hands-on GenAI experimentation in the last 12 months.
AI/GenAI Product Manager who has also led enterprise delivery and exec stakeholders.
Engagement Manager/Delivery Lead from a SaaS/AI product company who has shipped software and can do product thinking.
Senior Product Owner/TPM with strong stakeholder leadership plus clear GenAI build/eval work.

Nice To Haves

Evaluation harnesses, red-team testing, or model monitoring in production.
Regulated environments (healthcare/finance/insurance) and security/compliance constraints.
Change management: rollout, enablement, adoption metrics.
Professional services experience, especially fixed-price or outcome-based delivery.

Responsibilities

Own backlogs, prioritization, and sprint execution across 2–3 engagements.
Run standups, planning, retros, and cross-team coordination with engineering/design/data.
Write crisp user stories and acceptance criteria; make trade-offs under real constraints.
Ensure production readiness: security, compliance, data governance, deployment, and operational runbooks.
Drive post-launch iteration using user feedback, telemetry, and KPI performance.
Lead client working sessions and check-ins; steer decision-making with exec and practitioner stakeholders.
Translate ambiguity into clear plans, milestones, and measurable outcomes.
Own delivery health: scope, risks, dependencies, and expectation management.
Partner with the engagement lead/account team on renewals/expansions and executive readouts (no hard sales quota).
Help clients choose the right approach: workflow redesign, retrieval, tool use, evaluation strategy, rollout.
Design evaluation and measurement: rubrics, golden datasets, offline/online testing, and monitoring plans.
Make practical architecture-level recommendations with engineers (you don’t have to be the architect).
Stay current on model capabilities/limits and translate that into realistic product decisions.