Head of QA / Quality Engineering

Flam; Bengaluru Urban; On-site; INR 3,500,000 - 6,000,000; Full time

Job summary

Flam in Bengaluru is seeking a Head of QA to build and lead the quality engineering function, driving automation-first testing and AI/ML quality evaluation at scale. You will shape testing strategy, architecture, and governance, collaborating with Engineering, AI/ML, Product, DevOps, Security, and Customer Success to enable rapid delivery with reliability.

The role demands 10+ years in software testing/quality engineering and a track record in transforming QA organizations toward automation,

Qualifications

• 10+ years of experience in Software Testing / Quality Engineering.
• Experience testing AI/ML or Generative AI products.
• 4+ years in a senior QA leadership role managing and scaling QA organizations.
• Proven experience building or transforming a QA organization from manual QA toward automation and Quality Engineering.
• Strong experience with test automation architecture.
• Strong API, backend, frontend, and end-to-end testing experience.
• Experience designing CI/CD-integrated automated testing.
• Strong understanding of distributed systems and modern cloud applications.
• Experience with performance and scalability testing.
• Experience defining engineering quality metrics and release governance.

Responsibilities

• Build the Quality Engineering Function: define and own Flam's overall QA and Quality Engineering strategy, establish quality vision, processes, and standards, scale QA across manual testing, automation, performance, reliability, and testing types, define roles and career paths, foster a quality-focused culture, and partner with engineering leadership to embed quality in SDLC.
• Automation-First Testing: create automation strategy across frontend, backend, APIs, SDKs, and platform services; establish frameworks for UI/API/End-to-End/Contract/Mobile/Web testing; deeply integrate into CI/CD; define automation coverage and quality gates.
• AI / ML Quality & Evaluation: establish frameworks for evaluating AI/multimodal systems, including LLM evaluation, RAG evaluation, hallucination detection, retrieval quality, prompt/model regression testing, speech-to-text and voice quality, and automated AI evaluation pipelines.
• Performance, scalability & reliability testing: own quality strategy for scale, load/stress/soak testing, API performance, latency benchmarking, GPU/AI inference testing, resilience testing, capacity planning; set SLA/SLO metrics with production reliability.
• Release & Quality Governance: establish robust release process with quality gates and dashboards; ensure faster releases with high confidence; shift-left quality and establish data-driven defect management and root-cause analysis.
• Shift-Left Quality: introduce quality earlier in the lifecycle; promote unit/integration/contract/API-first testing; drive CI quality gates and production observability; own metrics like defect escape rate and automation coverage.
• Quality Engineering Architecture & Tooling: evaluate tooling and frameworks for test automation, API testing, performance testing, CI/CD integration, AI evaluation, synthetic data, environments, service mock/mocking, and observability.

Job description

Flam is building the next generation of interactive media through its content format. We are an AI-native technology company transforming how brands and consumers interact through immersive, interactive content. Our technology enables rich, app-less experiences that can be launched instantly on smartphones, creating a fundamentally different way for brands to engage consumers. We are backed by leading investors and already work with some of the world's largest brands. We are now building Flicks, our interactive media format for the US market.

The Role

As the Head of QA / Quality Engineering, you will own the overall quality strategy for Flam's products and technology platform. This is a hands-on leadership role that combines people leadership, engineering, automation, architecture, quality strategy, and operational excellence.You will work closely with Engineering, AI/ML, Product, DevOps, Security, and Customer Success to establish a world-class quality engineering organization capable of supporting rapid product development without compromising reliability —moving Flam from a primarily functional QA approach toward a modern, automation-first, engineering-led quality organization.

The ideal candidate can build the function from the ground up, define the right processes and metrics, introduce the right automation architecture, and personally drive the transformation.

Key Responsibilities

1. Build the Quality Engineering Function

• Define and own Flam's overall QA and Quality Engineering strategy.
• Establish the organization's quality vision, principles, processes, and engineering standards.
• Build and scale the QA organization across manual testing, automation, performance, reliability, and specialized testing.
• Define roles, responsibilities, career paths, and technical standards for the QA team.
• Establish a culture where quality is everyone's responsibility, while QA provides the engineering framework, tooling, and governance to achieve it.
• Partner with engineering leadership to embed quality throughout the software development lifecycle.

2. Automation-First Testing

• Build a comprehensive test automation strategy across frontend, backend, APIs, SDKs, and platform services, establishing automation frameworks for: UI Testing API Testing Integration Testing End-to-End Testing Regression Testing Contract Testing Mobile / Web Testing Significantly reduce dependency on manual regression testing.
• Integrate automated testing deeply into CI/CD pipelines.
• Establish appropriate test pyramids and testing standards across engineering teams.
• Define automation coverage and quality gates for every major product area.

3. AI / ML Quality & Evaluation

• Flam's products introduce testing challenges that go beyond traditional software QA. You will establish frameworks for evaluating AI and multimodal systems, including LLM response quality and consistency, RAG accuracy and relevance, hallucination detection and evaluation, retrieval quality, prompt and model regression testing, speech-to-text and text-to- speech quality, voice interaction reliability, avatar and lip-sync quality, computer vision and multimodal interaction quality, AI response latency, model/version regression, safety and undesirable model behavior, evaluation datasets and golden test sets, and automated AI evaluation pipelines.

4. Performance, Scalability & Reliability Testing

• Own the quality strategy for systems operating at scale, including load testing, stress testing, soak/endurance testing, scalability testing, API performance testing, concurrent-user testing, latency benchmarking, realtime interaction testing, GPU/AI inference performance validation, failure and recovery testing, resilience testing, and capacity testing.
• Establish clear SLA/SLO-oriented quality metrics and work with Engineering and DevOps to continuously improve production reliability.

5. Release & Quality Governance

• Establish a robust and predictable release process. You will define release quality gates, definition of done, regression strategy, risk-based testing methodology, release readiness criteria, production and post-release validation, defect severity and prioritization standards, escaped defect analysis, root-cause analysis, and release quality dashboards.
• The objective is to enable faster releases with higher confidence, rather than adding process and slowing engineering down.

6. Shift-Left Quality

• Partner with engineering teams to introduce quality earlier in the development lifecycle.
• Drive adoption of unit testing standards, integration testing, contract testing, API-first testing, developer-owned testing, static analysis, code qualitychecks, automated regression suites, CI quality gates, and production observability-driven quality improvement.
• QA should not be the final checkpoint before production; quality should be engineered into the product from the beginning.
• Establish a data-driven approach to defect management, owning metrics such as defect escape rate, regression rate, production defect rate, mean time to detect, mean time to resolve, defect recurrence, automation coverage, test execution time, release confidence, and flaky test rate.
• Drive systematic root-cause analysis for critical production issues and ensure corrective actions are implemented.

8. Quality Engineering Architecture & Tooling

• Evaluate and establish the appropriate tooling and frameworks for Flam's technology ecosystem, including test automation frameworks, API testing frameworks, performance testing platforms, CI/CD integration, test management and reporting, AI evaluation frameworks, synthetic test data, test environments, service virtualization/mocking, and monitoring and observability integration.

Work closely with Platform Engineering, AI/ML Engineering, Frontend Engineering, Product Management, DevOps/Infrastructure, Security, Customer Success, and business teams — representing quality at the leadership level and ensuring product velocity and product quality remain aligned.

What We Are Looking For

10+ years of experience in Software Testing / Quality Engineering.

Strongly Preferred

• Experience testing AI/ML or Generative AI products.
• Experience with LLM evaluation, RAG evaluation, or AItesting.
• 4+ years in a senior QA leadership role managing andscaling QA organizations.
• Proven experience building or transforming a QA organization from manual QA toward automation and Quality Engineering.
• Strong experience with test automation architecture.
• Strong API, backend, frontend, and end-to-end testing experience.
• Experience designing CI/CD-integrated automated testing.
• Strong understanding of distributed systems and modern cloud applications.
• Experience with performance and scalability testing.
• Experience defining engineering quality metrics and release governance.
• Experience working closely with engineering and product leadership.
• Experience with realtime systems.
• Experience testing voice, video, computer vision, or multimodal applications.
• Experience with high-scale consumer or enterprise SaaS platforms.
• Experience with Kubernetes/cloud-native environments.
• Experience building QA organizations in high-growth startups.
• Experience with security, reliability, or compliance-oriented testing.

We are looking for a leader who:

• Thinks like an engineer, not only a test manager.
• Is comfortable going deep into architecture and technology.
• Can write code and understand automation frameworks.
• Can challenge engineering teams constructively.
• Uses data rather than opinions to measure quality.
• Is comfortable operating in a fast-moving startup environment.
• Can balance speed, risk, and quality.
• Builds systems and processes rather than becoming a bottleneck.
• Can recruit, mentor, and develop high-performing QA engineers.
• Has strong ownership and execution orientation.
• Is comfortable dealing with ambiguity and building a function from the ground up