AI Product Leader

Blank Metal•Minneapolis, MN

2d•Hybrid

About The Position

The AI Product Leader works across product delivery, client engagement leadership, and applied AI strategy, with an approximate allocation of 40% product delivery, 40% client leadership, and 20% AI strategy. The role leads 2–3 concurrent engagements and is accountable for shipping, adoption, and measurable impact. The position owns backlogs, prioritization, sprint execution, team coordination, user stories, acceptance criteria, trade-offs, production readiness, and post-launch iteration. It also leads client working sessions and executive and practitioner stakeholder discussions, translates ambiguity into plans and measurable outcomes, and manages scope, risks, dependencies, and expectations. The role partners with engagement and account teams on renewals, expansions, and executive readouts, with no hard sales quota. For applied AI strategy, the role helps clients choose approaches involving workflow redesign, retrieval, tool use, evaluation, and rollout. It designs evaluation and measurement plans, makes practical architecture-level recommendations in partnership with engineers, and translates current model capabilities and limitations into realistic product decisions. Blank Metal is an AI-native engineering company that partners with enterprises to deliver production AI. The company combines strategic insight with hands-on execution to build AI applications intended to achieve measurable business results.

Requirements

Experience shipping software to production and the ability to describe what was built, what changed, and how it performed.
Ability to lead delivery, including budget, backlog, sprints, trade-offs, and stakeholder alignment.
Fluency in modern GenAI patterns, including prompting, RAG basics, evaluation, and iteration.
Hands-on experience using tools to build prototypes or workflows.
Ability to work amid ambiguity, make decisions with incomplete requirements and imperfect information, and adapt as learning develops.
Relevant background as an AI/GenAI Product Manager who has led enterprise delivery and executive stakeholders; an Engagement Manager or Delivery Lead from a SaaS/AI product company who has shipped software and can apply product thinking; or a Senior Product Owner/TPM with strong stakeholder leadership and clear GenAI build/evaluation work.

Nice To Haves

Experience with evaluation harnesses, red-team testing, or production model monitoring.
Experience in regulated environments such as healthcare, finance, or insurance, including security and compliance constraints.
Change-management experience involving rollout, enablement, and adoption metrics.
Professional services experience, especially fixed-price or outcome-based delivery.
Experience with AI-powered low-code platforms such as V0, Bolt, or Lovable for rapid prototyping and stakeholder demos.
AI experience through GenAI product work, or adjacent data/ML product work combined with clear hands-on GenAI experimentation in the last 12 months.

Responsibilities

Own backlogs, prioritization, and sprint execution across 2–3 concurrent engagements.
Run standups, planning, retrospectives, and coordination across engineering, design, and data teams.
Write user stories and acceptance criteria, and make trade-offs under real constraints.
Ensure production readiness across 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, and steer decisions with executive and practitioner stakeholders.
Translate ambiguity into clear plans, milestones, and measurable outcomes.
Own delivery health, including scope, risks, dependencies, and expectation management.
Partner with engagement and account teams on renewals, expansions, and executive readouts.
Help clients select applied AI approaches, including workflow redesign, retrieval, tool use, evaluation strategy, and rollout.
Design evaluation and measurement plans, including rubrics, golden datasets, offline and online testing, and monitoring plans.
Make practical architecture-level recommendations with engineers.
Stay current on model capabilities and limitations and apply that knowledge to product decisions.