AI Product Manager

About Us We are a technology services company dedicated to helping organizations build and deploy cutting-edge AI solutions. From generative AI and custom LLM integrations to predictive analytics and intelligent automation, we work across industries to bring real-world AI applications to life. Our projects combine deep technical expertise with hands-on client collaboration to solve high-impact problems. Job Summary We are seeking an AI Product Manager to serve as the technical counterpart to our business development team and the owner of AI product delivery across our client portfolio. The AI PM partners closely with BD to scope and validate what we sell—then owns delivering it. This role spans ChatGPT Enterprise adoption programs and custom API/agent engagements, requiring someone who can translate between C-suite business goals and engineering constraints without pretending to be either. It’s the right role 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. Key Responsibilities 1. Business Development Partnership & Scoping Serve as the technical counterpart to sales throughout the sales process, helping scope what is feasible, what the path to production looks like, and what a realistic engagement structure should be. Handle the strategic and feasibility layer of technical conversations with prospects and clients: use case fit, sequencing, data requirements, timeline realism, and risk. Know where the PM lane ends. When conversations move into deep engineering territory (infrastructure architecture, API integration specifics, security requirements), pull in the right engineer and keep the overall conversation connected to business outcomes. Ensure that what gets scoped and sold is what can actually be delivered, preventing commitments that do not survive contact with reality. 2. Use Case Discovery & Prioritization Run structured discovery with client stakeholders within active engagements to surface AI use cases, working across business units to understand pain points, workflows, and data landscape. Build and maintain a scored use case backlog for each engagement, evaluating opportunities against feasibility, data readiness, and measurable business impact. Make clear go/no-go recommendations on what is ready for AI and what is not, grounding those calls in an honest assessment of current model capabilities and client maturity. 3. Product Definition & Delivery Own the end-to-end lifecycle of AI products from scoping through production launch, including requirements definition, prompt and agent architecture decisions, and acceptance criteria. Write clear product specs that translate business problems into technical requirements engineering can build against, covering inputs, outputs, constraints, and success metrics. Manage the gap between demo and production: identify edge cases, compliance requirements, data quality issues, and scalability risks early and build plans around them. Drive iterative development cycles, working hands-on with prompt engineering and agent design decisions alongside the technical team. 4. Defining Success & Measuring Outcomes Own the definition of what success looks like for every AI deployment, connecting model performance to the business outcomes the client actually cares about. Work with client SMEs to establish domain-specific success criteria for probabilistic systems where success is not binary and evaluation is iterative. Track and report on product performance post-launch, including adoption, business outcomes, and continuous improvement opportunities. 5. Stakeholder Management Serve as the connective tissue between business stakeholders and engineering, ensuring technical teams build what matters and business leaders understand what is possible. Lead client-facing working sessions to align on scope, priorities, and tradeoffs, translating complex AI concepts into clear, honest language without overselling. Prepare and deliver executive-level updates on product progress, risks, and impact, keeping communication simple and outcome-oriented. 6. AI Center of Excellence Contribution Contribute to repeatable playbooks for AI use case prioritization, governance, and production readiness deployed across our client portfolio. Help shape the methodology for how enterprises move from AI experimentation to production at scale, codifying what works into frameworks and templates. Stay current on the evolving AI platform and tooling landscape—models, orchestration frameworks, vector databases, monitoring—and bring that perspective into client strategy. Qualifications Required 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—what LLMs and agents can and cannot do—and the ability to update that mental model as the technology evolves. Proven ability to navigate technical conversations credibly without being an engineer: ask the right questions, assess feasibility, and know when to escalate. Strong written and verbal communication skills—clear, direct, and free of jargon when working with both executive stakeholders and technical teams. Experience managing multiple concurrent client engagements or projects without letting quality slip. Preferred Hands-on experience with ChatGPT Enterprise, OpenAI API, Anthropic, or similar LLM platforms. Familiarity with prompt engineering, agent design patterns, or orchestration frameworks (e.g., LangChain, LlamaIndex). Prior consulting, professional services, or client-facing delivery experience. Familiarity with enterprise data infrastructure, compliance considerations, or AI governance frameworks. What We Offer Competitive compensation (base salary + performance incentives tied to client outcomes). Equity options in a growing AI services company. Exposure to a wide range of industries and high-impact AI problems. Travel opportunities for on-site client engagements (if desired). A collaborative, mission-driven team passionate about the real-world impact of AI.