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AI Product Owner & Quality Engineer

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AI Product Owner & Quality Engineer Course

AI is redefining how digital products are built, tested, and delivered. Our AI Product Owner & Quality Engineer Course at Transfotech Academy bridges product strategy and AI innovation teaching you how to design, evaluate, and govern AI-driven products responsibly. Over 8 weeks, you’ll master AI story-writing, prompt design, experimentation, and model governance using both no-code and light-code tools. Guided by industry experts, you’ll learn to manage AI features, lead technical teams, and make data-backed decisions preparing you to thrive in the next generation of intelligent product management.

Course Structure Overview

Hands-on video lectures

Understand how AI integrates into real product and quality workflows with guided examples.

Collaborative support community

Work with mentors, peers, and facilitators through reviews and weekly group activities.

Live Q&A sessions and peer review labs

Refine your skills and projects through continuous assessment and expert insights.

Digital guidebook

Get access to structured templates, datasets, worksheets, and project documentation.

What you will learn by doing the course

AI Product Ownership and Management combine creativity, governance, and analytical thinking. By the end of this course, you’ll be able to:

  • Write AI-ready user stories with measurable acceptance criteria (ACs).
  • Design prompt packs, evaluate outputs, and apply guardrails for safety.
  • Develop synthetic datasets and run golden test evaluations.
  • Build traceability between PRDs, test cases, and outcomes.
  • Design A/B experiments and interpret decision metrics.
  • Govern AI features using catalogs, audit trails, and incident playbooks.
  • Lead data-driven teams with clarity, ethics, and accountability.

Details about the course

  • Understand LLM strengths, limits, and practical product applications.
  • Learn to convert tickets and analytics into outcome statements.
  • Write AI-ready stories and measurable Gherkin acceptance criteria.
  • Implement telemetry and safety requirements for AI products.
  • Create Opportunity and Outcome documentation for stakeholders.
  • Understand system vs user roles in prompting.
  • Learn prompt versioning, constraints, and refusal handling.
  • Design structured JSON schema for reproducible prompts.
  • Create and test your first Prompt Pack for AI task automation.
  • Establish prompt versioning and output validation best practices.
  • Develop synthetic datasets with labeled oracles for evaluation.
  • Apply risk-based testing principles for AI-driven scenarios.
  • Design edge cases, adversarial prompts, and coverage strategies.
  • Build datasets in spreadsheets or light-code environments.
  • Document data integrity and PII minimization practices.
  • Build golden test sets and define evaluation metrics.
  • Compare exact vs semantic scoring methods.
  • Set up performance thresholds, SLAs, and fallback mechanisms.
  • Develop guardrail frameworks for latency, cost, and refusal rates.
  • Create an evaluation harness to measure prompt and model quality.
  • Convert PRDs into AI-ready stories, ACs, and test plans.
  • Automate SQL/API checks using Postman and query templates.
  • Learn triage, repro, and reporting for product quality.
  • Map traceability between acceptance criteria, data, and validation tests.
  • Manage AI-assisted grooming and QA workflows.
  • Build Experiment Cards with hypotheses and guardrail metrics.
  • Run simulated A/B tests and interpret experimental outcomes.
  • Learn KPI tracking and dashboard planning for AI performance.
  • Write decision memos to justify product iterations or launches.
  • Use metrics-based evidence for product improvement and scaling.
  • Maintain AI catalogs and model version control systems.
  • Perform red-team testing to uncover prompt and model vulnerabilities.
  • Update guardrails based on incident results and evaluation failures.
  • Draft end-to-end incident playbooks (triage → rollback → postmortem).
  • Implement safety protocols and governance policies for deployment.
  • Plan and assemble your final AI Product Pack (PRD + Prompt Pack + Eval Set).
  • Conduct governance and safety checks for your deployment plan.
  • Develop presentation slides highlighting metrics and decision outcomes.
  • Present your AI product for review and feedback from instructors.
  • Receive certification upon successful project presentation and evaluation.

Course Instructor

Fayek Chowdhury

Fayek Chowdhury

Instructor, Transfotech Academy

With over a decade of experience, Fayek serves as an AI Product Owner and Quality Engineer, specializing in software testing and quality assurance at top companies like Veruna Inc. and OUTFRONT Media. His expertise in QA automation, AI-driven quality processes, and agile methodologies has consistently delivered impactful results. Passionate about mentorship and innovation, he is dedicated to shaping the next generation of QA Engineers and driving excellence in AI-powered product development.

AI Product Owner & Quality Engineer
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What is in this course

  • 8-Week Job-Ready Program
  • 16 Interactive Live Sessions (Concept + Lab)
  • No-code and Light-code learning paths
  • Step-by-step project-based curriculum
  • Real-world governance and QA use cases
  • Capstone AI Product Development Project
  • Instructor-led mentorship and peer feedback

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