AI Product Manager

About the Role We

are looking for an experienced AI Product Manager to own the vision, strategy, and delivery of our Generative AI and data products. You will lead a team of product managers and work together with engineering, data science, design, and business stakeholders to take AI products from concept to production and scale on public cloud platforms and with measurable business impact. This is a role for a product leader who combines deep AI/ML fluency with proven people management: someone who has shipped GenAI solutions end to end, understands data and machine learning fundamentals well enough to challenge technical trade-offs, and can build and grow a high-performing product team.

What You Will Do

Product strategy and vision

• Define and own the product strategy and roadmap for AI and GenAI products, aligned with business objectives and customer needs.

• Identify, evaluate, and prioritize AI use cases: assess feasibility, value, data readiness, and risk; decide where GenAI is the right tool and where it is not.

• Translate ambiguous business problems into clear product requirements, success metrics, and measurable outcomes (adoption, quality, cost, ROI).

• Track the evolving AI landscape (foundation models, agents, tooling, regulation) and turn it into practical product opportunities and competitive positionin

GenAI product delivery

• Lead end-to-end delivery of GenAI products: discovery, prototyping, evaluation, pilot, production rollout, and iteration.

• Define requirements specific to LLM-based products: prompt and RAG behavior, grounding and accuracy expectations, evaluation criteria, human-in-the-loop workflows, guardrails, and fallback behavior.

• Partner with engineering and data science on model selection (proprietary vs. open source), build-vs-buy decisions, latency/cost/quality trade-offs, and architecture choices on public cloud platforms (Azure, AWS, or GCP).

• Establish evaluation and quality frameworks for AI features: offline evals, A/B testing, user feedback loops, and continuous monitoring of model and product performance.

• Own Responsible AI from the product side: safety, fairness, transparency, privacy, and compliance requirements built into the product lifecycle.

Data and ML product foundations

• Work with data teams to define data requirements, data quality standards, and feedback/labeling loops that power ML and GenAI products. Page 1 of 3 Confidential - for candidate use only

• Manage products across the broader ML lifecycle classical ML, predictive analytics, and GenAI understanding training, fine-tuning, deployment, and MLOps implications for roadmap and delivery.