Job Description & How to Apply Below
Role Overview
# AI QA Program Manager
Experience - 8+Years
Location - Bengaluru,
kindly revert with your updated CV at email
- We are looking for an AI QA Program Manager to own the planning, execution, and delivery of AI evaluation and QA initiatives across multiple customers and internal products.
This role sits at the intersection of program management, AI QA strategy, and client delivery. You will ensure that AI evaluation programs—covering GenAI behavior, reliability, safety, and quality—are executed predictably, reported clearly, and aligned with business outcomes.
This is not a traditional QA manager role. It requires comfort working with GenAI systems, evolving requirements, and cross-functional technical teams, while maintaining strong delivery discipline.
Key Responsibilities
- Own end-to-end delivery of AI QA and evaluation programs across clients and internal initiatives
- Plan and track:
- Evaluation scope and timelines
- Resource allocation (AI QA engineers, leads, specialists)
- Milestones and deliverables
- Act as the primary coordination point between:
- AI QA teams
- Product and ML engineering teams
- Platform/Dev Ops teams
- Client stakeholders
- Ensure clear, high-quality reporting of evaluation results, risks, and recommendations
- Manage dependencies, risks, and scope changes in fast-evolving AI projects
- Standardize program execution frameworks, templates, and best practices for AI QA
- Support pre-sales and solution discussions by translating requirements into executable programs
Must-Have Skills & Experience
- 6–10+ years of experience in program management, delivery management, or QA program leadership
- Experience managing multi-team, multi-stream technical programs
- Strong communication skills with both technical teams and business stakeholders
- Experience working in services, consulting, or client-facing delivery models
- Solid understanding of software testing or QA fundamentals
Good-to-Have
- Experience working with AI/ML or GenAI systems
- Familiarity with AI evaluation concepts (accuracy, hallucination, bias, safety, robustness)
- Experience in early-stage or rapidly evolving programs
- Exposure to security, compliance, or risk-focused QA
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