Director Engineering - Automation, Performance and AI Quality

So, what’s the role all about?

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

• 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

Have you got what it takes?

• 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 tools (Datadog, Grafana, Dynatrace, New Relic)

Leadership

• Exceptional communicator — able to translate deep technical concepts into executive-level strategy and roadmaps
• Strong organizational influencer who shapes culture and engineering standards beyond your direct org
• Data-driven decision maker: you measure quality outcomes, not just activity
• Bias for hiring and developing senior talent; you build teams that outlast you
• Comfortable operating in ambiguity and driving clarity in fast-moving environments