Director, Quality & Release Engineering

• Job title: Director, Quality & Release Engineering • Location: London or Bristol (Hybrid Office - 3+ days per week) • Salary: £150,000 - £180,000 • Team: Quality & Release • Level: Director This role is based in London or Bristol and requires existing right to work in the UK. At this time, we are not able to offer visa sponsorship for this role. We are committed to building a diverse, global team and our sponsorship policy is evaluated on a role-by-role basis. We encourage you to keep an eye on our careers site to stay informed about future opportunities where we are able to offer visa sponsorship. Kaluza is the Energy Intelligence Platform, turning energy complexity into seamless coordination. We help energy companies overcome today’s challenges while accelerating the shift to a clean, electrified future. Our platform orchestrates millions of real-time decisions across homes, devices, markets and grids. By combining predictive algorithms with human-centred design, Kaluza makes clean energy dependable, affordable and adaptive to everyday life. With teams across Europe, North America, Asia and Australia, and a joint venture with Mitsubishi Corporation in Japan, we power leading companies including OVO, AGL and ENGIE, as well as innovators like Volvo and Volkswagen. What will I be doing? The quality engineering function is shifting from manual oversight and process governance toward an AI-assisted product development lifecycle where QA leads its own domain test strategy and bespoke tooling drives automation maturity. This role will drive this shift in our organization and ways of working. You will own quality and release governance at programme level, directly manage an engineering team building AI-assisted automation and test data capability, and set the standard that QA leads across delivery streams work to. You will be as comfortable driving change as you are accountable for outcomes. Responsibilities will include: Strategic Quality Leadership • Own and evolve the quality strategy across delivery streams, aligned to programme objectives and client SLAs • Define and maintain release governance frameworks — release readiness criteria, risk acceptance, and sign-off authorities • Set quality standards and coverage expectations; ensure QA leads translate these into domain-level test strategies • Drive defect governance at programme level — internal and client-facing SLA compliance • Represent quality and risk in senior leadership, architecture, and product forums AI-Driven Tooling & Automation • Directly manage a team of engineers building and maintaining bespoke automation tooling, leveraging AI and LLM capabilities • Own the test data management strategy — including AI-assisted synthetic data generation and environment data integrity • Evaluate and select off-the-shelf AI testing tools; define governance frameworks for their use and validation • Ensure governance gates are maintained as AI participation in the PDLC grows — accountable humans must remain in the loop for risk acceptance and production judgement • Establish tooling standards that QA leads can adopt across domains without creating dependency on a central bottleneck QA Function Leadership & Operating Model • Lead, develop, and performance-manage QA leads embedded across domains; define the operating model and career framework for the function • Own the accountability split clearly: QA leads own test strategy and tooling choices for their domain; this role owns the standard they work to • Drive consistency across streams without becoming a handoff point — enable QA leads to move fast within well-defined guardrails • Build a quality engineering culture — not a bug-finding one Change Management & Adoption • Lead the transformation of QA practice from script maintenance to intelligent, risk-based, AI-assisted quality engineering • Drive upskilling across the function — building capability in AI tooling, context engineering, and agent-assisted workflows • Manage resistance and build confidence in new ways of working; bring the team through change without losing them • Communicate quality outcomes in business terms to senior stakeholders — escaped defect rates, release stability, risk posture Release Management • Own the release quality process end-to-end: environment strategy, regression coverage, go/no-go decisions, and post-release monitoring • Embed shift-left practices — quality gates early in the PDLC, not just at release • Manage the tension between delivery velocity and release stability, particularly as AI-generated code volumes increase About You Ideally you’ll have/be • Proven track record leading a QA or quality engineering function of significant scale — including embedded domain leads and direct management of practitioners • Has directly managed engineers building tooling or automation infrastructure — not just managed QA practitioners executing test plans • Has delivered a meaningful QA transformation: AI adoption, automation maturity uplift, defect governance overhaul, or similar • Has worked in a multi-client or multi-product platform environment, managing quality outcomes across concurrent delivery streams • Has owned release governance in a complex, multi-stream delivery context — including go/no-go accountability • Exposure to energy, utilities, billing, or similarly regulated complex domains is a plus — not essential Strong grounding in test strategy, automation frameworks, and CI/CD integration — sufficient to credibly lead engineers building these things • Working knowledge of LLMs and AI tooling in a QA context: test generation, defect triage, coverage analysis, synthetic test data • Understands test data management principles and the challenges of environment data integrity at scale • Able to evaluate build vs. buy for tooling; assess AI testing platforms with appropriate scepticism Comfortable setting strategy and being accountable for outcomes — not just own