AVP - AI Quality Engineering

Build the Future of Intelligent Quality - Own the Business - Shape Market Thinking

This is a Line of Business leadership role, not a QE engineering role.

Location: India (Hyderabad) | Experience: 18+ Years | Type: Full-Time

Quality Engineering is no longer a support function it is becoming a strategic differentiator in an AI-first world.

At Innominds, we are at a pivotal moment where AI is transforming how quality is engineered (faster, predictive, autonomous), and AI systems themselves demand a new standard of trust, validation, and governance.

We are looking for a Delivery leader, not just an engineering expert someone who can own, build, and scale AI Quality Engineering as a Line of Business, while shaping how enterprises think about quality in the AI era.

THE ROLE:

As Engineering Delivery Leader for AI Quality Engineering, you will operate simultaneously across four dimensions. This is where strategy meets execution, and vision meets scale.

Business Owner:

- Accountable for growth, P&L, and market success of the QE practice

Market Shaper:

- Influencing how clients and partners perceive AI-driven quality

Transformation Delivery Leader:

- Moving QE from reactive testing to intelligent, predictive engineering

Practice Builder:

- Creating a high-performance, future-ready QE organisation

- This role is built on three interconnected mandates. Each one depends on the others. Together, they define what it means to lead AI Quality Engineering at Innominds.

- Define the Future : Set the vision for AI-driven Quality Engineering internally and in the market. Where the discipline is going, what Innominds' position is, and how we get there first.

- Enable the Business : Create and scale a differentiated QE portfolio with measurable revenue, margin, and growth. Own the P&L, the pipeline, and the commercial outcomes of the practice.

- Deliver Trust at Scale :Ensure every engagement especially AI-led systems meets a high bar of quality, reliability, and responsibility. Quality is the promise Innominds makes to every client.

KEY RESPONSIBILITIES:

1. Own & Scale the QE Line of Delivery:

- Define and drive the multi-year vision and growth strategy for AI Quality Engineering as a distinct line of business

- Build a market-relevant service portfolio aligned to the needs of ISV, enterprise, and AI-native clients

- Own Delivery ,CSAT , revenue targets, margin discipline, and headcount investment for the QE practice

- Identify where to build capabilities, which partnerships to pursue, and which emerging AI QE trends to bet on ahead of the market

2. Lead the Shift to AI-First Quality Engineering:

- Champion and drive the fundamental shift from manual, script-heavy QE to AI-powered, predictive, and continuously learning quality systems

- Establish standardised AI QE methodologies across all engagements - making intelligent quality the default operating model, not an isolated initiative

- Define and deliver clear business outcomes from AI-augmented quality: faster release cycles, lower cost-of-quality, higher defect prevention rates

- Govern the evaluation and adoption of AI QE tooling - making deliberate choices about what the practice builds, buys, and partners to deliver

3. Build Industry-Leading AI Quality Capabilities:

- Define the frameworks, evaluation methodologies, and governance standards for testing AI/ML systems - LLMs, ML models, RAG pipelines, and AI agents

- Establish trust standards for AI products: accuracy, fairness, explainability, observability, and safety - as non-negotiable quality gates

- Package AI quality validation, red-teaming, and model governance as commercial service offerings clients actively seek out

- Stay ahead of the evolving AI regulatory landscape (EU AI Act, responsible AI standards) and translate requirements into executable quality practices

4. Drive Client Impact & Market Growth:

- Act as the executive face of AI Quality Engineering for clients a trusted advisor to CTOs, VPs of Engineering, and Chief Quality Officers

- Lead high-value client conversations: QE transformation roadmaps, AI quality strategy, and executive briefings that position quality as a business differentiator

- Own the QE pipeline, lead presales engagements, and close deals through an insight-led, differentiated value proposition

- Build and activate partner relationships with hyperscalers and QE ISVs - creating co-sell motions that expand pipeline and market reach

5. Build a High-Impact QE Organization:

- Attract, develop, and retain a high-caliber team of QE engineers, automation architects, and AI quality specialists

- Create a practice culture defined by engineering craftsmanship, curiosity, and a shared commitment to raising the quality bar

- Invest in the intellectual capital of the practice: reusable AI QE accelerators, evaluation playbooks, and thought leadership that differentiates Innominds in the market

- Drive delivery governance and operational excellence - the practice should be as well-run internally as the quality bar it holds clients to

WHAT SUCCESS LOOKS LIKE?

- QE evolves into a high-growth, high-margin line of business with a clear market identity

- AI-led quality becomes the default delivery model across Innominds' programmes - not an exception

- Clients see Innominds as the go-to trusted partner for AI quality, reliability, and responsible AI validation

- The practice builds recognisable market presence through thought leadership, partnerships, and lighthouse wins

- Quality outcomes are directly linked to business outcomes - speed, trust, and competitive advantage for every client

WHO THIS ROLE IS FOR?

You Bring:

- 15+ years in Quality Engineering or Software Engineering, with at least 5 years owning a QE practice, team, or business unit

- Proven experience building or scaling a QE practice into a revenue engine - with P&L, commercial, or business ownership accountability

- A strong, informed point of view on how AI is transforming software quality and the SDLC - and the ability to articulate it with conviction

- The ability to operate comfortably at both levels: boardroom strategy and delivery execution, without losing credibility at either

- Proven presales and deal-shaping experience - owning QE opportunity pipelines and closing through insight, not just relationship

- Credibility with senior engineering teams and the influence to shape thinking at CXO level

- Experience working with hyperscaler or QE platform partners in a co-sell or joint GTM capacity

You Stand Out If:

- You have a clear, articulated vision for where AI Quality Engineering is going - and you can walk into any room and shift how senior leaders think about the space

- You have built or transformed a QE practice from a cost centre into a differentiated, commercial capability

- You have hands-on exposure to AI system validation, LLM evaluation, AI agent testing, or responsible AI frameworks in a production context

- You actively contribute to the industry conversation - through published writing, conference talks, or recognised thought leadership in quality engineering

- You hold active relationships with hyperscaler or QE platform partners, and understand how to turn those into joint business outcomes

Why This Role Matters?

Quality is no longer a checkpoint - it is a strategic lever for speed, trust, and competitive differentiation. This role gives you the opportunity to build a next-generation QE business, shape industry standards for AI quality, and influence how modern engineering organisations operate at scale.

This is not a role for someone who wants to manage testing. This is for someone who wants to redefine quality as a business and a competitive advantage.

KPIs THAT DEFINE YOUR SUCCESS:

- KPI

- Target

- Frequency

- Pillar

- QE Practice Revenue Growth

- 25% YoY growth in QE line of business

Quarterly

Business

Presales Win Rate

- 35% conversion on AI QE proposals

Quarterly

Business

Thought Leadership Output

- 4 published assets or speaking slots / year

Semi-Annual

Business

AI-Augmented Coverage Ratio

- 60% of test coverage AI-assisted across programmes

Quarterly

Transformation

Defect Escape Rate

< 2% post-release defects across QE-led programmes

Monthly

Transformation

Release Cycle Time Reduction

- 40% faster regression cycles vs. baseline

Quarterly

Transformation

AI System Validation Coverage

100% of AI products have evaluation framework at launch

Per Release

AI Quality

LLM Hallucination Rate

< 3% on production AI systems tested by Innominds

Monthly

AI Quality

Customer Satisfaction (CSAT)

- 4.5 / 5 across all QE engagements

Quarterly

Client

Account Expansion Rate

- 20% of accounts expand scope within 12 months

Quarterly

Client

Team Retention

< 12% attrition within the AI QE practice

Quarterly

Practice

Reusable QE Assets Delivered

- 4 accelerators / frameworks per year

Semi-Annual

Practice

This is not a role for someone who wants to manage testing.

This is for someone who wants to redefine quality as a business - and a competitive advantage.