Define and own the AI product vision, roadmap, and success metrics, aligned with business goals and AI maturity strategy. Translate business problems into AI-suitable problem statements, clearly defining objectives, constraints, and expected value. Lead AI initiatives through the full lifecycle: discovery, design, development, validation, deployment, and post-launch optimization.
Key Highlights
Technical Skills Required
Job Description
Job Title: AI Product Owner
Skills: AI product vision, roadmap, and success metrics, aligned with business goals and AI maturity strategy, AI Delivery & Lifecycle Management
Experience: 10+ Years
Location: Remote – Someone in East Coast, preferably near the Connecticut area, with the requirement to visit the client’s location as needed.
Job Type: Fulltime
We at Coforge are hiring for AI Product Owner with the following skills:
Role Overview
The AI Product Owner (AI PO) is responsible for defining, prioritizing, and delivering AI‑driven products and capabilities that solve real business problems and create measurable value. This role bridges business stakeholders, data science/engineering teams, and platform teams, translating business needs into AI‑ready product requirements while ensuring ethical, scalable, and high‑quality AI solutions across the product lifecycle.
The AI Product Owner owns the product vision, backlog, roadmap, and value realization for AI initiatives, including Generative AI, ML, and agent‑based solutions.
Key Roles & Responsibilities
Product Strategy & Vision
- Define and own the AI product vision, roadmap, and success metrics, aligned with business goals and AI maturity strategy
- Identify and prioritize high‑impact AI use cases that deliver measurable outcomes such as cost reduction, revenue uplift, productivity gains, or risk mitigation
- Translate business problems into AI‑suitable problem statements, clearly defining objectives, constraints, and expected value
Backlog & Requirements Management
- Convert high‑level requirements into Epics, Features, and User Stories with clear acceptance criteria suitable for AI/ML development
- Own and continuously groom the AI product backlog, ensuring prioritization based on business value, feasibility, and risk
- Ensure requirements address data needs, model behavior, performance thresholds, explainability, and compliance considerations
Stakeholder Collaboration
- Act as the primary interface between business stakeholders, AI engineers, data scientists, and platform teams
- Facilitate alignment on trade‑offs between model accuracy, cost, scalability, latency, and delivery timelines
- Communicate AI capabilities and limitations clearly to non‑technical stakeholders to set realistic expectations
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AI Delivery & Lifecycle Management
- Lead AI initiatives through the full lifecycle: discovery, design, development, validation, deployment, and post‑launch optimization
- Partner with engineering teams to support MLOps, model retraining strategies, and monitoring of AI performance in production
- Ensure human‑in‑the‑loop (HITL) controls and governance are embedded where required
Quality, Ethics & Governance
- Ensure AI solutions adhere to responsible AI principles, including fairness, transparency, security, and regulatory compliance
- Define acceptance criteria for model accuracy, bias thresholds, explainability, and operational reliability
- Support audits, documentation, and governance reviews for AI systems
Value Tracking & Continuous Improvement
- Define and track KPIs and success metrics for AI products post‑deployment
- Gather user feedback and operational insights to continuously enhance AI models and workflows
- Drive iterative improvements and backlog refinement based on real‑world performance and adoption
Required Skills & Qualifications
Product & Agile Skills
- Strong experience as a Product Owner / Product Manager in Agile or SAFe environments
- Expertise in backlog management, user story writing, prioritization, and release planning
- Experience working with cross‑functional, distributed teams
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AI & Technical Understanding
- Solid understanding of AI/ML concepts, including Generative AI, LLMs, supervised/unsupervised learning, and agent‑based systems
- Ability to work closely with data scientists and engineers without needing to build models hands‑on
- Familiarity with data pipelines, model evaluation metrics, MLOps concepts, and cloud AI platforms
Business & Analytical Skills
- Strong ability to connect AI capabilities to business outcomes and justify ROI
- Experience defining value hypotheses, KPIs, and success measures for AI initiatives
- Excellent problem‑solving, analytical, and decision‑making skills
Communication & Leadership
- Excellent stakeholder communication and storytelling skills—able to explain AI concepts in simple, business‑friendly terms
- Proven ability to influence without authority and drive alignment across teams
- Strong documentation and presentation skills
Preferred / Nice‑to‑Have
- Experience delivering GenAI, Conversational AI, RAG, or AI agent solutions
- Exposure to regulated industries (Financial Services, Healthcare, Insurance)
- Familiarity with Responsible AI frameworks, data privacy regulations, and AI governance models
Success Measures for the Role
- AI products delivering measurable business value post‑go‑live
- High adoption and stakeholder satisfaction
- Well‑governed, scalable, and reliable AI solutions in production
- Clear alignment between AI roadmap and enterprise strategy
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