About the Role Weare 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 AIML 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 tradeoffs and can build and grow a highperforming product teamWhat 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 positioninGenAI product delivery Lead endtoend delivery of GenAI products discovery prototyping evaluation pilot production rollout and iteration Define requirements specific to LLMbased products prompt and RAG behavior grounding and accuracy expectations evaluation criteria humanintheloop workflows guardrails and fallback behavior Partner with engineering and data science on model selection proprietary vs open source buildvsbuy decisions latencycostquality tradeoffs and architecture choices on public cloud platforms Azure AWS or GCP Establish evaluation and quality frameworks for AI features offline evals AB 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 lifecycleData and ML product foundations Work with data teams to define data requirements data quality standards and feedbacklabeling 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 finetuning deployment and MLOps implications for roadmap and delivery Define pricing packaging and cost models that account for inference and cloud consumption economicsPeople and stakeholder leadership 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 tradeoffs 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 gotomarket collaboration with sales and marketing RequirementsRequired Qualifications 810 years of overall experience spanning product management AIML and data with at least 45 years in product management roles Proven people management experience directly managing hiring and developing product managers or crossfunctional teams Demonstrated track record of delivering GenAI products or projects to production LLMpowered applications copilots RAGsearch 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 Handson familiarity with the GenAI stack foundation models OpenAIGPT Claude Gemini Llama DeepSeek Kimi GLM etc prompt engineering RAG finetuning 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 consumptionbased pricing and SaaS delivery models Strong product craft discovery user research roadmap management backlog prioritization PRD writing and datadriven decision making experimentation analytics KPIs Understanding of Responsible AI AI governance and data privacy considerations eg GDPR emerging AI regulation as they apply to product decisions Excellent communication and executive presence able to present to Clevel stakeholders influence without authority and simplify complex AI concepts for any audience Experience with agile delivery at scale and modern product tooling JiraAzure DevOps Figma analytics platforms Preferred Qualifications Prior hand
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