AI Product Manager - AIRLKLHV

NavitasPartners
Remote

About The Position

We are seeking an experienced AI Product Manager to lead the strategy, development, and delivery of AI-powered products across enterprise environments. This role focuses on translating business challenges into scalable AI solutions and driving adoption from concept through production. The ideal candidate combines strong product management expertise with a solid understanding of AI technologies, enabling them to guide teams in building practical, high-impact solutions rather than experimental prototypes.

Requirements

  • Proven experience as a Product Manager, Technical Product Manager, or AI Product Owner
  • Experience delivering AI, ML, analytics, or data-driven products in enterprise environments
  • Working knowledge of generative AI, machine learning, LLMs, and related technologies
  • Strong ability to translate business needs into technical requirements
  • Experience managing product roadmaps, backlogs, and release cycles
  • Excellent stakeholder management and communication skills
  • Experience taking at least one AI/ML or analytics product from concept to production
  • Ability to assess AI feasibility and guide stakeholders toward practical use cases
  • Strong decision-making and prioritization skills in ambiguous environments

Nice To Haves

  • Experience with RAG architectures, model evaluation, and AI deployment practices
  • Background in consulting or fast-paced enterprise environments
  • Familiarity with governance, compliance, and responsible AI frameworks
  • Experience working with cross-functional teams in data, engineering, and business domains

Responsibilities

  • Define AI product vision, roadmap, success metrics, and delivery priorities aligned with business goals
  • Identify and prioritize high-value AI use cases across operations, customer experience, and business processes
  • Translate business requirements into product features, user stories, and technical backlogs
  • Collaborate with data scientists, AI engineers, data engineers, and UX teams to deliver production-ready solutions
  • Evaluate feasibility, risks, and value of AI initiatives before development
  • Manage the full product lifecycle from discovery to deployment, adoption, and continuous improvement
  • Establish governance frameworks including responsible AI, privacy, and model performance monitoring
  • Communicate progress, risks, and outcomes to stakeholders and executive leadership
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