(US) Product Owner – AI products & projects

You will own the vision, roadmap, and delivery of intelligent, AI-native products & solutions that turn complex enterprise workflows into simple, assistive experiences. You’ll operate at the intersection of product strategy, applied AI/ML, Generative AI and platform engineering - translating cutting-edge capabilities into measurable business value.

1) Define the Vision & Roadmap

•            Own a problem-first roadmap for AI-powered platforms and assistants across priority journeys.

•            Comprehensive market research & prioritize use cases with clear value hypotheses (efficiency, revenue, quality) and Revenue Roadmap.

2) UAT first development

•            Run structured discovery (persona, journey mapping, workflow time-and-motion).

•            Author detailed UAT document for each feature with detailed acceptance criteria, test cases and evaluation metrics.

3) Product Leadership

•            Lead backlog, sprint goals, and release planning with AI/ML, data, and platform engineering.

•            Manage dependencies (data readiness, NFRs, MLOps, security, integrations) and unblock teams rapidly.

4) Measure, Learn, Iterate

•            Define north-star & guardrail metrics: accuracy, latency, cost per request, containment, adoption, and satisfaction.

•            Drive experimentation (A/B), prompt/model iterations, UX refinements, and policy updates.

5) Bridge Business & Technology

•            Translate model behavior and system constraints into business-speak (value, risk, ROI).

•            Create readable artifacts: PR/FAQ, release notes, change logs, and exec updates.

6) Safety, Compliance & Observability

•            Partner with Security/Legal to enforce PII/PHI handling, data residency, SOC2/ISO controls.

•            Ensure traceability (prompt versioning, dataset lineage, evaluation reports) and human-in-the-loop in product thinking wherever needed.

•            2+ years building or shipping AI/ML/GenAI products & solutions (NLP, RAG/semantic search, recommendations, predictive, or automation).

•            Practical exposure to LLMs, vector databases, prompt engineering, and cloud AI services (well familiar with Azure OpenAI, AWS Bedrock, GCP Vertex).

•            Proven agile delivery using Jira/ Confluence/ Azure DevOps; design collaboration in Figma.

•            Strong systems thinking with the ability to simplify complex workflows.

•            Strong academic background + MBA (Tier-1) engineering/CS preferred.

•            Experience with agentic systems (tool-use, function calling, planners), prompt orchestration, and knowledge retrieval frameworks.

•            Familiarity with enterprise integrations, APIs, and event-driven architectures.

•            Understanding of MLOps/LLMOps (evaluation harnesses, offline/online tests, drift monitoring, canary releases).

•            Product: Roadmapping, PRD/PR-FAQ, OKRs, KPI design, experimentation.

•            AI/Tech: RAG patterns, embeddings, retrieval tuning, prompt/policy design, latency/cost trade-offs.

•            Governance: Prompt/version control, LLMOps, evaluation dashboards.

•            Collab: Azure DevOps, Figma, Lucidchart, PPT, Excel, Word, Git.