Competitive, depending on experience
Brazil (remote)
The Principal AI Quality Engineer provides strategic AI-first quality leadership for Lhasa Software Development at portfolio level. As the technical quality lead of a solutions team, they own quality engineering standards, AI quality governance, and AI-human collaboration patterns across Lhasa, and design and govern the multi-agent test orchestration that delivers consistent quality at scale. The fundamentals of quality engineering have not changed; the Principal AI Quality Engineer sets the standard for how AI becomes the primary mechanism for achieving them, with human judgement, exploratory testing, and direct authorship applied where the work demands it.
The Principal AI Quality Engineer sets the standard for AI-first quality engineering across the function, brings deep quality judgement across test architecture, systems thinking, and risk reasoning, mentors the wider Software Development team, and contributes to the quality roadmap and enterprise AI quality strategy.
- Set the quality guardrails, test architecture standards, AI harness patterns, and acceptance frameworks for AI testing agents portfolio-wide
- Design and govern multi-agent test workflows where specialist AI testing agents handle quality assurance across all test types under appropriate human oversight; define test specifications, acceptance criteria, and quality gates
- Own the design and governance of automated quality gates in the development pipeline; ensure quality-by-design is embedded from the point of AI code generation
- Validate AI-generated test coverage, quality architectures, and test strategies at portfolio level; intervene directly on complex scientific validation, performance, and security testing
- Manage AI testing reliability and hallucination risks at scale; design appropriate human-in-the-loop checkpoints for situations where AI testing alone is insufficient
- Direct AI testing agents and harnesses to design, test, and support software across the Life Sciences domain; apply deep domain knowledge to validate the result
- Carry out manual, exploratory, and non-functional testing where AI tooling is unlikely to find the right issues, or as a check on AI-generated output
- Embed quality early and continuously in the software development lifecycle across multiple products
- Take ownership of complex quality problems across the portfolio and ensure they are successfully resolved
- Lead Quality Excellence strategy, continuous improvement, and outcomes across Lhasa Software Development
- Define decision criteria and quality constraints; use AI-generated evidence to inform strategic quality decisions in collaboration with Principal AI Engineers, Product Owners, and Delivery Managers
- Maintain a deep understanding of AI testing capabilities and limitations; design AI-human quality collaboration models that balance automation with human judgement at portfolio scale
- Define AI testing agent capability requirements and orchestration frameworks; own the quality roadmap contribution for Software Development; collaborate on enterprise AI quality governance
- Own test context engineering standards, AI harnesses, and governance across Lhasa; ensure quality strategy is embedded in agent infrastructure and not dependent on any single individual
- Maintain knowledge continuity at portfolio level: externalise quality knowledge into documentation, ADRs, AI harnesses, and agent configurations so the discipline is not dependent on any single individual
- Lead the development and iteration of quality engineering standards and guidance in collaboration with senior colleagues
- Serve as the quality engineering lead for a solutions team, providing direction, setting standards, and being accountable for quality outcomes across the portfolio
- Develop the quality engineering capability of the Software Development function for AI test orchestration; build AI-human quality collaboration skills across teams; focus mentorship on outcome definition, quality judgement, and AI testing limitation awareness
- Pioneer and champion best practices observed within Lhasa and externally; raise the quality bar across the function
- Identify quality gaps across the portfolio and own the plans to address them
- Contribute to product architecture from a quality perspective; collaborate with the Architecture Team
- Technically lead the quality engineering discipline within Lhasa alongside other Technical Leaders
- Communicate clearly with delivery teams and senior stakeholders; keep all parties informed of quality progress, risks, and decisions at portfolio level
- Cascade quality strategy and engineering standards across Software Development; ensure alignment between quality direction and business goals
- Represent Software Development across Lhasa and with senior stakeholders where required
- Act as a key quality contact for all delivery team members, including those external to Software Development
- Actively contribute to the Software Development community of practice
- Lead by example as the role model for AI-first quality engineering across the function; champion Lhasa’s values
- Design innovative AI-human quality collaboration models; identify opportunities for expanded AI testing capability; keep current with advances in AI testing technologies
- Be accountable for the quality excellence of solutions from design through to production operations
- Live the cultural values of Ownership, Integrity, Collaboration, Diversity & Inclusivity, and Curiosity & Adaptability
- Undertake additional tasks and responsibilities which may be reasonably expected of the role
- Promote the visibility of Lhasa within professional networks and at external events
The following responsibilities may apply based on business need and individuals’ aspirations, skills, and suitability:
- People leadership responsibilities may apply based on business need and individual aspiration.
- Undertaking additional tasks to achieve the outcomes of the Software Development function may apply based on business need.
AI-first, quality-minded, and strategic. Collaborative, professional, and supportive. Takes full accountability for quality outcomes at portfolio scale, thinks beyond the immediate task to the broader business and quality outcome, and continuously seeks better ways to use AI to accelerate and improve quality delivery. Models openness to change at a strategic level and shapes quality engineering culture across Software Development. Conscientious with strong attention to detail and committed to continuous professional development in a rapidly evolving field.
- Degree level or equivalent in Computer Science, Mathematics, Chemistry, or a related discipline
- Exceptional commercial track record of leading quality engineering at system or portfolio level in lieu of degree
- Professional certification in software testing (e.g. ISTQB Advanced or Expert)
- Proven ability to design AI test orchestration workflows, AI harnesses, and validate AI-generated quality architectures at portfolio level
- Deep expertise in AI test orchestration platforms, multi-agent test system architecture, test context engineering, AI harnesses, and the capabilities and limitations of AI testing
- Deep and extensive commercial experience in quality engineering, with a proven track record of technical leadership and strategic influence
- Strong knowledge of test architecture patterns and quality systems thinking at portfolio level
- Practical experience designing, writing, and maintaining automated tests across the full stack, the craft that underpins effective AI direction and validation
- Experience designing and delivering quality assurance for distributed systems or enterprise-scale applications
- Experience of non-functional testing (performance, security, accessibility, reliability) at enterprise scale
- Experience designing AI-augmented Agile quality workflows and AI quality governance frameworks
- Experience with at least one modern backend language (e.g. Java, Python, TypeScript/Node.js), cloud platform (e.g. AWS), CI/CD tooling (e.g. Jenkins, Bitbucket Pipelines), and containerisation (e.g. Docker, Kubernetes); breadth across the stack matters more than depth in any single tool
- Working knowledge of AI coding/testing assistants, AI code-review tooling, and multi-agent orchestration frameworks; keeping current with this fast-evolving landscape is expected
- Experience of building testing frameworks and quality gates for AI-generated code at portfolio level
- Experience of acting as a quality engineering lead of a solutions team
- Experience of testing software products in a scientific domain
- Experience of performance and line management in a matrix environment
