We are looking for a visionary engineering leader to architect and lead our quality engineering discipline into the AI era. You will own automation infrastructure, performance engineering, and all AI-driven quality initiatives – building and mentoring high-performing teams of engineers and architects who set the standard for how modern software is validated at scale.
This is not a maintenance role. You will disrupt legacy testing paradigms, champion agentic test generation, and embed AI-quality thinking into every stage of the SDLC – from the first commit to production monitoring. You will be the organizational bridge between engineering velocity and product reliability.
How will you make an impact?
AI Quality Strategy:
Define and lead all AI-augmented quality initiatives across the organization
Deploy LLM-powered test generation, triage, and root-cause analysis tooling
Build frameworks for validating AI/ML model outputs and non-deterministic behaviors
Evaluate and integrate emerging AI quality toolchains (GitHub Copilot, Claude, agents)
Champion responsible AI testing practices, coverage standards, and governance models
Automation Engineering:
Own the full automation strategy across UI, API, and integration layers
Architect scalable, maintainable test frameworks
Drive shift-left testing embedded in CI/CD pipelines (Jenkins, GitHub Actions)
Establish coverage KPIs, flakiness SLAs, and automation ROI metrics
Lead adoption of self-healing and AI-assisted test maintenance
Performance Engineering:
Lead performance and load testing strategy at enterprise scale
Define SLOs/SLAs and partner with SRE on reliability thresholds
Design continuous performance validation integrated into delivery pipelines
Drive root-cause analysis for regressions, bottlenecks, and latency spikes
Oversee toolchain including k6, JMeter, Gatling, and APM integrations
People & Org Leadership:
Lead and grow teams of automation engineers, performance engineers, and architects
Set technical direction, OKRs, and career ladders for the quality engineering org
Hire senior ICs and architect-level talent; build succession depth
Foster a culture of ownership, engineering craft, and continuous learning
Partner cross-functionally with Product, DevOps, and R&D leadership.
This is not a maintenance role. You will disrupt legacy testing paradigms, champion agentic test generation, and embed AI-quality thinking into every stage of the SDLC – from the first commit to production monitoring. You will be the organizational bridge between engineering velocity and product reliability.
How will you make an impact?
AI Quality Strategy:
Define and lead all AI-augmented quality initiatives across the organization
Deploy LLM-powered test generation, triage, and root-cause analysis tooling
Build frameworks for validating AI/ML model outputs and non-deterministic behaviors
Evaluate and integrate emerging AI quality toolchains (GitHub Copilot, Claude, agents)
Champion responsible AI testing practices, coverage standards, and governance models
Automation Engineering:
Own the full automation strategy across UI, API, and integration layers
Architect scalable, maintainable test frameworks
Drive shift-left testing embedded in CI/CD pipelines (Jenkins, GitHub Actions)
Establish coverage KPIs, flakiness SLAs, and automation ROI metrics
Lead adoption of self-healing and AI-assisted test maintenance
Performance Engineering:
Lead performance and load testing strategy at enterprise scale
Define SLOs/SLAs and partner with SRE on reliability thresholds
Design continuous performance validation integrated into delivery pipelines
Drive root-cause analysis for regressions, bottlenecks, and latency spikes
Oversee toolchain including k6, JMeter, Gatling, and APM integrations
People & Org Leadership:
Lead and grow teams of automation engineers, performance engineers, and architects
Set technical direction, OKRs, and career ladders for the quality engineering org
Hire senior ICs and architect-level talent; build succession depth
Foster a culture of ownership, engineering craft, and continuous learning
Partner cross-functionally with Product, DevOps, and R&D leadership.
Requirements:
12+ years in software engineering or QA, with 5+ years in engineering leadership at the Director level or senior Staff+ with direct reports
Deep hands-on background in test automation architecture – you've built frameworks from scratch, not just inherited them
Proven track record leading performance engineering at scale (10,000+ concurrent users, distributed systems, cloud-native environments)
Experience shipping or operating AI/ML systems and understanding the unique quality challenges they introduce
Prior success managing multi-team orgs with both IC engineers and architects (10-40+ total headcount)
Enterprise software domain experience strongly preferred (SaaS, CCaaS, fintech, or similar high-uptime/regulated environments)
AI & Technical Acumen:
Working knowledge of LLM capabilities and limitations relevant to software testing (prompt engineering, RAG, agentic workflows)
Experience evaluating or integrating AI coding assistants (GitHub Copilot, Claude, Cursor, or equivalents) in engineering workflows
Comfort with statistical testing concepts: model drift detection, data validation, behavioral testing for non-deterministic outputs
Proficient in at least one scripting/programming language (Python, TypeScript, Java) and modern CI/CD tooling
Familiarity with observability and APM Familiarity with observability and APM tools (Datadog, Grafana, Dynatrace, New Relic).
12+ years in software engineering or QA, with 5+ years in engineering leadership at the Director level or senior Staff+ with direct reports
Deep hands-on background in test automation architecture – you've built frameworks from scratch, not just inherited them
Proven track record leading performance engineering at scale (10,000+ concurrent users, distributed systems, cloud-native environments)
Experience shipping or operating AI/ML systems and understanding the unique quality challenges they introduce
Prior success managing multi-team orgs with both IC engineers and architects (10-40+ total headcount)
Enterprise software domain experience strongly preferred (SaaS, CCaaS, fintech, or similar high-uptime/regulated environments)
AI & Technical Acumen:
Working knowledge of LLM capabilities and limitations relevant to software testing (prompt engineering, RAG, agentic workflows)
Experience evaluating or integrating AI coding assistants (GitHub Copilot, Claude, Cursor, or equivalents) in engineering workflows
Comfort with statistical testing concepts: model drift detection, data validation, behavioral testing for non-deterministic outputs
Proficient in at least one scripting/programming language (Python, TypeScript, Java) and modern CI/CD tooling
Familiarity with observability and APM Familiarity with observability and APM tools (Datadog, Grafana, Dynatrace, New Relic).
This position is open to all candidates.
