Senior AI Product Manager

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.

• 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

• 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.

• 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.

• Define pricing, packaging, and cost models that account for inference and cloud consumption economics.

• Manage, coach, and grow a team of product managers; set goals, run performance reviews, and build career paths.

• Establish product management practices and rituals: discovery methods, prioritization frameworks, PRD standards, and delivery cadences.

• Drive alignment across engineering, data science, design, security, legal, and business units; manage executive stakeholders and communicate progress, risks, and trade-offs clearly.

• Act as the voice of the customer: run user research, customer interviews, and pilots with enterprise or end users.

• Evangelize AI products internally and externally: demos, enablement, launch plans, and go-to-market collaboration with sales and marketing.

• 8–10+ years of overall experience spanning product management, AI/ML, and data with at least 4–5 years in product management roles.

• Proven people management experience: directly managing, hiring, and developing product managers or cross-functional teams.

• Demonstrated track record of delivering GenAI products or projects to production LLM-powered applications, copilots, RAG/search experiences, agents, or conversational AI not just prototypes.

• Strong working knowledge of machine learning and data fundamentals: model lifecycle, training vs. inference, evaluation metrics, data pipelines, and analytics; able to engage credibly with data scientists and ML engineers.

• Hands-on familiarity with the GenAI stack: foundation models (OpenAI/GPT, Claude, Gemini, Llama, DeepSeek, Kimi, GLM, etc.), prompt engineering, RAG, fine-tuning concepts, vector search, and agent frameworks.

• Experience building and shipping applications on public cloud platforms (Azure, AWS, or GCP), including understanding of cloud AI services, consumption-based pricing, and SaaS delivery models.

• Strong product craft: discovery, user research, roadmap management, backlog prioritization, PRD writing, and data-driven decision making (experimentation, analytics, KPIs).

• Understanding of Responsible AI, AI governance, and data privacy considerations (e.g., GDPR, emerging AI regulation) as they apply to product decisions.

• Excellent communication and executive presence: able to present to C-level stakeholders, influence without authority, and simplify complex AI concepts for any audience.

• Experience with agile delivery at scale and modern product tooling (Jira/Azure DevOps, Figma, analytics platforms).

• Prior hands-on technical background (data science, ML engineering, software engineering) before moving into product.

• Experience with Azure AI ecosystem (Azure AI Foundry, Azure OpenAI, Azure ML) or equivalent AWS/GCP AI platforms.

• Experience taking AI products through enterprise sales cycles, pilots, or regulated industry deployments. • Familiarity with LLMOps/MLOps practices and AI evaluation tooling.

• Experience defining AI product pricing and unit economics (token/inference cost management, FinOps for AI).

• MBA or advanced degree in computer science, data science, or related field.

• Certifications such as cloud AI certifications (AI-102, AWS ML Specialty, Google ML Engineer) or product management credentials.

• Experience in [industry e.g., financial services, telecom, healthcare, government].

• Strategic thinking paired with hands-on execution and delivery discipline • Sound judgment on AI feasibility, risk, and value knows when to ship, iterate, or stop

• Servant leadership: grows people, builds trust, and raises the bar on product quality

• Comfort with ambiguity and a fast-moving technology landscape

• Outcome orientation: measures success in business impact, not features shipped