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# Technical Product Owner

- **Company:** Wipro Limited
- **Location:** Chicago, IL, United States
- **Experience:** Expert
- **Salary:** $60,000.0 - $135,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Python (Programming Language), Raw Data, Software Engineering, SQL Databases, Retrieval-Augmented Generation, Large Language Models, IT Architecture, Generative AI, Agentic-AI, Gherkin, Information Technology, Data Analytics, Performance Monitor, Claude, Azure AI, GPT
- **Published:** October 21, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=20199eb7ac1cc2af

## About the Role

Required Skills: · AI/ML Acumen: Familiarity with Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), vector databases, or predictive modeling (basic data literacy like SQL or Python is often preferred). · AI-Augmented Workflow: Experience using AI tools (such as Claude, ChatGPT, Azure AI, or agentic frameworks) to draft user stories, simulate edge cases, and run pre-mortem/what-if analyses. · Stakeholder Management: Ability to translate dense technical architecture into business outcomes for leadership, and business constraints into clear guidance for engineers

Preferred Qualifications: · Education: Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Mathematics, or a related business field. · Experience: 5+ years in product management, software development, management consulting, or data analytics, with direct exposure to AI/ML products.

Mandatory Skills: GCP AI Architecture .

Experience: 5-8 Years .

## Description

Key Responsibilities: · Value & Problem Definition: Identify high-impact use cases where AI/ML adds genuine user value rather than implementing AI just because it is trendy. · Data & Model Collaboration: Partner closely with data scientists, ML engineers, and data analysts to define dataset requirements, cleanliness, labeling, and accuracy thresholds. · Backlog Management & Prioritization: Structure Agile backlogs to accommodate iterative experimentation, raw data collection, model training, validation, and fallback logic. · User Stories & Acceptance Criteria: Translate complex AI capabilities (such as LLMs, RAG, or agentic workflows) into clear, INVEST-compliant user stories, test cases, and Gherkin-style acceptance criteria. · Responsible AI & Governance: Ensure compliance with data privacy regulations and AI frameworks (e.g., GDPR, EU AI Act) by auditing for bias, transparency, and human-in-the-loop safeguards. · Performance Monitoring: Track post-launch model drift, user
adoption, accuracy metrics, and business ROI