Associate Director Quality Engineering

A quality engineering leader who combines strategic thinking with technical judgment. Someone who can influence teams without relying solely on authority, challenge decisions constructively, identify risks early, and make quality an integral part of software delivery through automation, engineering discipline, AI-driven testing, and shared ownership.

We are looking for an experienced Associate Director – Quality Engineering to strengthen engineering quality across our products and platforms. The role combines technical depth, quality strategy, automation architecture, AI-driven testing, and strong engineering judgment. The successful candidate will drive quality earlier in the development lifecycle, identify risks before they reach production, and work closely with Engineering, Product, DevOps, and other stakeholders to improve release confidence. This is a senior technical leadership role focused on engineering excellence, defect prevention, intelligent automation, and measurable quality improvements.

Quality strategy and ownership: Define quality engineering standards, KPIs, testing principles, and release-readiness criteria. Drive shift-left testing across requirements, design, code reviews, and development workflows.

Technical leadership and collaboration: Participate in architecture and design reviews; assess risks and dependencies; challenge implementation or release decisions constructively when significant quality risks remain unresolved.

Defect prevention and RCA: Lead root-cause analysis for critical defects and production incidents, drive preventive actions, and reduce recurring issues and customer impact.

Automation architecture: Design and evolve maintainable automation for web, API, mobile, and integrations using tools such as Playwright, TypeScript, Python, Selenium, and relevant API-testing frameworks.

Test strategy: Establish effective coverage across unit, integration, API, contract, and end-to-end testing. Improve test reliability, reduce flaky tests, and shorten feedback cycles.

AI-native quality engineering: Introduce AI-assisted test design, automation development, debugging, and defect analysis. Ensure generated tests and automation code are reviewed and validated before adoption.

LLM, RAG, and agent testing: Define evaluation for correctness, groundedness, relevance, consistency, hallucinations, prompt injection, unsafe outputs, tool-calling behaviour, access-control boundaries, and failure recovery where applicable.

CI/CD and release readiness: Integrate automated quality gates into CI/CD pipelines; define risk-based release criteria, regression strategies, go/no-go recommendations, and post-release verification.

Performance and reliability: Validate latency, throughput, concurrency, scalability, and resilience under peak traffic. Partner with Engineering, DevOps, and SRE to address bottlenecks and improve reliability.

Team development: Mentor quality engineers and technical leads, strengthen engineering standards, support hiring and technical assessments, and encourage shared ownership of quality across development and QA.

10+ years in software engineering, SDET, test automation, or quality engineering, with demonstrated senior technical leadership across multiple teams.

Strong understanding of automation architecture and frameworks such as Playwright, Selenium, or equivalent.

Hands-on experience in API, integration, contract, and end-to-end testing.

Working knowledge of TypeScript/JavaScript, Python, Java, or similar programming languages.

Experience integrating automated testing into CI/CD pipelines and modern software delivery workflows.

Understanding of distributed systems, microservices, asynchronous workflows, databases, and third-party integrations.

Strong skills in production debugging, root-cause analysis, defect prevention, and release-risk management.

Experience using AI-assisted engineering tools to improve testing productivity and automation effectiveness.

Excellent communication, analytical thinking, and the ability to challenge decisions constructively using evidence.

Hands-on experience evaluating LLM-based applications, RAG systems, AI agents, and tool-calling workflows.

Familiarity with Ragas, DeepEval, promptfoo, or equivalent AI evaluation frameworks.

Experience with performance tools such as k6 or JMeter, and observability platforms, cloud infrastructure, or deployment automation.

Experience in high-traffic consumer platforms, food delivery, e-commerce, SaaS, or other distributed product environments.

Reduction in production defect leakage and customer-impacting incidents.

Improved automation reliability, meaningful coverage, and execution time.

Faster CI/CD feedback and increased release confidence.

Earlier identification of engineering risks and fewer recurring defects.

Improved AI-feature evaluation coverage and detection of quality regressions.

Stronger engineering productivity and adoption of shared quality practices.