AI Product Manager

About the role

You will serve as the technical counterpart to our business development team and the owner of AI product delivery across our client portfolio. The role requires you to partner closely with BD to scope and validate what we sell, then own the delivery of those commitments from conversation to production. You will translate between C-suite business goals and engineering constraints while maintaining credibility in both worlds without pretending to be either. This position is ideal for a sharp, structured thinker who thrives on ambiguity, communicates with clarity, and knows how to get AI products across the finish line in the real world. You will own end-to-end accountability for AI product outcomes, ensuring that what is promised is technically feasible and aligned with client value. You will act as the primary decision owner for prioritization, scope, and acceptance within active engagements. The role demands comfort with probabilistic outputs, evolving model behavior, and managing risk in production AI systems. You will represent the voice of the client and the integrity of the product through every delivery phase.

Key facts

What you'll do

• Partner with business development through the full sales cycle to scope feasibility, sequence implementation, and define realistic engagement structures for AI initiatives.
• Conduct strategic and feasibility conversations with prospects, covering use case fit, data readiness, timeline realism, and risk exposure before commitments are made.
• Draw clear boundaries where your role ends and engineering depth begins, ensuring deep technical decisions are routed to the right specialist while preserving coherent business outcomes.
• Prevent commitments that cannot survive contact with reality by rigorously aligning sales promises with delivery capabilities and model behavior.
• Lead structured discovery with client stakeholders to surface AI use cases, capturing pain points, workflows, and underlying data landscapes across business units.
• Build and maintain a scored use case backlog for each engagement, evaluating opportunities by feasibility, data readiness, and measurable business impact.
• Make clear go/no-go recommendations on which use cases are ready for AI, grounding decisions in honest assessments of current model capabilities and client maturity.
• Own the end-to-end lifecycle of AI products from scoping through production launch, including requirements, prompt and agent architecture, and acceptance criteria.
• Translate business problems into precise technical specifications that engineering can build against, covering inputs, outputs, constraints, and success metrics.
• Manage the gap between prototype and production by identifying edge cases, compliance requirements, data quality issues, and scalability risks early.
• Drive iterative development cycles in collaboration with prompt engineering and agent design teams, refining behavior through testing and feedback.
• Define and own success criteria for each AI deployment, linking model performance to client business outcomes in environments where success is probabilistic.
• Work with client subject matter experts to establish domain-specific evaluation criteria and iterate on measurement approaches post-launch.
• Track and report on product performance after launch, including adoption, realized business outcomes, and continuous improvement opportunities.
• Act as the connective tissue between business stakeholders and engineering, ensuring alignment on priorities and translating complex AI concepts into clear, honest language.
• Lead client-facing working sessions to align on scope, tradeoffs, and expectations while maintaining trust and transparency.
• Prepare and deliver executive-level updates on product progress, risks, and impact using language that is simple, outcome-oriented, and free of unnecessary hype.
• Contribute to repeatable playbooks for AI use case prioritization, governance, and production readiness across the client portfolio.
• Help shape methodology for moving enterprises from AI experimentation to production at scale, codifying effective practices into frameworks and templates.
• Stay current on the evolving AI platform and tooling landscape, including models, orchestration frameworks, vector databases, and monitoring systems, and bring that insight into client strategy.

Requirements

• 3+ years of experience in product management, technical program management, or a closely related role, with direct exposure to AI or ML products.
• Working knowledge of modern AI systems, including model types, training and inference patterns, and common deployment architectures.
• Demonstrated ability to translate ambiguous business problems into structured product requirements and success metrics.
• Experience conducting discovery and scoping sessions with enterprise stakeholders across multiple functions.
• Strong written and verbal communication skills, with the ability to explain complex AI concepts to both technical and non-technical audiences.
• Comfort working with probabilistic outputs, iterative evaluation, and metrics in domains where ground truth is not immediately available.
• Track record of owning end-to-end delivery for at least one production AI or data-intensive product.
• Understanding of data requirements, quality challenges, and governance considerations in production AI systems.
• Ability to make objective prioritization decisions balancing business impact, feasibility, risk, and timeline constraints.
• Experience with prompt engineering, agent design patterns, or LLM operations is required.
• Familiarity with at least one cloud platform and modern tooling for model deployment and monitoring is required.
• Willingness to work closely with engineering and data science teams, diving into details when necessary while keeping the broader business context in view.
• Commitment to ethical AI practices, including transparency about system limitations and responsible deployment considerations.
• Willingness to operate within a structured product development framework while adapting methods to fit varied client environments.
• Readiness to maintain and evolve playbooks, templates, and artifacts that support repeatable AI delivery at scale.
• Capacity to manage multiple engagements simultaneously while maintaining clarity on priorities and risks.
• Willingness to continuously update technical and domain knowledge as models, platforms, and best practices evolve.

Nice to have

• Experience in regulated domains such as finance, healthcare, or public sector where compliance and risk management are critical.
• Background in building and maintaining AI Center of Excellence functions inside large organizations.
• Familiarity with specific orchestration frameworks, vector databases, or model monitoring tools that accelerate production deployments.
• Prior work developing methodology for moving organizations from AI experimentation to production at scale.

Practical notes

This is a full-time engagement based in the United States. The role may require travel to client sites as needed within the United States. No visa sponsorship is available for this position at this time. The candidate must be able to start within the timeframe aligned with business needs.