About the role
Role
At StarCompliance, we build software that supports critical compliance needs for global clients. AI is now a core capability within our platform, embedded directly into our products and services.
We are looking for an experienced AI Quality Engineering Lead to drive modern, automation-first quality engineering across our global engineering organisation. This highly technical leadership role will focus on AI-assisted testing, agentic engineering, automation strategy, performance validation, and release confidence across cloud-native and legacy platforms.
This is not a traditional QA management position. You will partner closely with Engineering, Architecture, DevOps, Product, and Release Management teams to embed quality throughout the software development lifecycle and enable scalable, continuous delivery.
How We Think About AI
At StarCompliance, AI is a core part of how we build and operate modern SaaS platforms.
We expect our engineers to:
- Use AI-assisted engineering tools in daily workflows.
- Apply AI to improve development speed, automation, operational insight, and engineering quality.
- Ensure AI-generated outputs remain secure, compliant, auditable, and reliable.
Key Responsibilities
AI & Agentic Quality Engineering
Lead adoption of AI-assisted testing tools and agentic engineering workflows.
Design and implement autonomous and semi-autonomous testing agents.
Develop best practices for AI-generated testing, intelligent validation, and automated quality analysis.
Enable engineering teams to embed AI-driven quality practices into day-to-day delivery.
Platform & Release Quality
Own release quality strategy across products and services.
Build scalable end-to-end, integration, and regression testing approaches.
Improve release confidence through automation, telemetry, and quality intelligence.
Performance & Scalability Testing
Define and execute performance, load, stress, and scalability testing strategies.
Validate reliability across distributed and high-volume environments.
Identify bottlenecks and support engineering teams in improving platform resilience.
Quality Engineering Enablement
Establish frameworks and standards for modern quality engineering.
Partner with development teams to strengthen automation and quality ownership.
Mentor engineers in automation, AI-assisted testing, and agentic engineering practices.
Metrics & Quality Intelligence
Define meaningful quality metrics aligned to operational and customer outcomes.
Build dashboards focused on release health, reliability, and platform performance.
Use data-driven insights to identify risks and drive continuous improvement.
