Location: London or Bristol, UK (hybrid, three days a week in the office)

Level: Director

Team: Quality & Release

Salary: £150,000 - £180,000 + discretionary bonus

Contract: Permanent

Reporting To: VP Engineering

Please note: This role requires an existing right to work in the UK. We can’t offer visa sponsorship, but we review sponsorship role by role, so keep an eye on our careers site for future opportunities.

About Kaluza

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, 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 you’ll do

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