AI Product Manager

You will translate ambiguous business problems into well-scoped product bets, partner closely with ML engineers, data scientists, and designers, and ship features that pair strong user experience with responsible, reliable AI. You will be equally comfortable writing a crisp PRD, reasoning about model and retrieval trade-offs, and presenting outcomes to enterprise and government stakeholders.\nKEY RESPONSIBILITIES\nOwn the product vision, strategy, and roadmap for assigned AI products, and align it with company and client objectives.\nTranslate business and user problems into clear product requirements, user stories, and acceptance criteria for AI and agentic features.\nPartner with ML engineers and data scientists on model selection, retrieval-augmented generation (RAG), evaluation, and context/architecture decisions.\nDefine and track success metrics — adoption, accuracy, latency, cost-per-task, and business impact — and use them to prioritize the backlog.\nLead discovery: customer interviews, market and competitor analysis, and opportunity sizing across healthcare, real estate, legal-tech, fintech, and public-sector verticals.\nManage the full delivery lifecycle in an agile environment — grooming, sprint planning, and release — working with engineering and QA.\nEstablish guardrails for responsible AI: data privacy, evaluation, human-in-the-loop review, hallucination control, and security/compliance requirements.\nCollaborate with pre-sales, design, and delivery teams on demos, proposals, and client roadmaps, including RFP and government-sector engagements.\nCommunicate roadmap, trade-offs, and outcomes clearly to executives, clients, and cross-functional teams.\nREQUIRED QUALIFICATIONS\n5–8 years of product management experience, with at least 2 years building AI, ML, or data-driven products.\nDemonstrated experience shipping products built on LLMs, generative AI, or agentic/RAG systems from concept to production.\nStrong grasp of AI/ML concepts — prompting, embeddings, vector search, fine-tuning vs. RAG, evaluation, and model/cost trade-offs — enough to make informed product decisions.\nProven ability to write clear PRDs, define metrics, and run agile delivery with engineering teams.\nExcellent stakeholder management and communication skills, including working with enterprise or government clients.\nBachelor’s degree in Computer Science, Engineering, Data Science, or a related field (or equivalent practical experience).\nPREFERRED / NICE TO HAVE\nExperience with cloud AI platforms (Azure, AWS, GCP) and modern AI stacks (LangGraph, FastAPI, vector databases).\nBackground in a regulated or domain-specific vertical such as healthcare, fintech, legal-tech, or public sector.\nExposure to multi-tenant SaaS, marketplace, or platform products.\nMBA or relevant product/technical certifications.\nHands-on familiarity with analytics and experimentation tooling.\nWHAT WE OFFER\nOwnership of cutting-edge AI products with real enterprise and government impact.\nA collaborative AI Smartz team working at the frontier of agentic AI.\nCompetitive compensation, learning budget, and clear growth into senior product leadership.