{"id":2833729,"url":"https://alion.io/job/lhasa-limited-principal-ai-quality-engineer","title":"Principal AI Quality Engineer","company":{"id":2398211,"name":"Lhasa Limited","domain":"lhasalimited.org","url":"https://alion.io/company/lhasalimited","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Schema","truth_index":{"grade":"B","score":75,"open_postings":3,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-28T05:59:10Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":[],"countries":[],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Context Engineering","optional":false},{"name":"Hallucination","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Agile","optional":true},{"name":"AWS","optional":true},{"name":"Bitbucket","optional":true},{"name":"CI/CD","optional":true},{"name":"Docker","optional":true},{"name":"Java","optional":true},{"name":"JavaScript","optional":true},{"name":"Jenkins","optional":true},{"name":"Kubernetes","optional":true},{"name":"Multi-Agent Systems","optional":true},{"name":"Node JS","optional":true},{"name":"Python","optional":true},{"name":"TypeScript","optional":true}],"status":"live","first_seen_at":"2026-07-23T00:00:00Z","employer_posted_date":"2026-10-12","last_verified_at":"2026-10-28T13:59:28Z","board_verified":true,"closed_at":null,"days_open":97,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":97},"description":"Principal AI Quality Engineer (Brazil based)\nCompetitive, depending on experience\n\nPermanent, Full Time\n\nBrazil (Remote)\n\nSalary\nCompetitive, depending on experience\nLocation\nBrazil (remote)\nSummary of the role\nThe 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.\nThe 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.\nMain Responsibilities\nAI-First Quality Engineering\nSet the quality guardrails, test architecture standards, AI harness patterns, and acceptance frameworks for AI testing agents portfolio-wide\nDesign 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\nOwn 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\nValidate AI-generated test coverage, quality architectures, and test strategies at portfolio level; intervene directly on complex scientific validation, performance, and security testing\nManage AI testing reliability and hallucination risks at scale; design appropriate human-in-the-loop checkpoints for situations where AI testing alone is insufficient\nDirect AI testing agents and harnesses to design, test, and support software across the Life Sciences domain; apply deep domain knowledge to validate the result\nCarry 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\nEmbed quality early and continuously in the software development lifecycle across multiple products\nTake ownership of complex quality problems across the portfolio and ensure they are successfully resolved\nStrategy and Quality Architecture\nLead Quality Excellence strategy, continuous improvement, and outcomes across Lhasa Software Development\nDefine 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\nMaintain a deep understanding of AI testing capabilities and limitations; design AI-human quality collaboration models that balance automation with human judgement at portfolio scale\nDefine AI testing agent capability requirements and orchestration frameworks; own the quality roadmap contribution for Software Development; collaborate on enterprise AI quality governance\nOwn 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\nMaintain 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\nLead the development and iteration of quality engineering standards and guidance in collaboration with senior colleagues\nTechnical Leadership\nServe as the quality engineering lead for a solutions team, providing direction, setting standards, and being accountable for quality outcomes across the portfolio\nDevelop 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\nPioneer and champion best practices observed within Lhasa and externally; raise the quality bar across the function\nIdentify quality gaps across the portfolio and own the plans to address them\nContribute to product architecture from a quality perspective; collaborate with the Architecture Team\nTechnically lead the quality engineering discipline within Lhasa alongside other Technical Leaders\nCommunication and Collaboration\nCommunicate clearly with delivery teams and senior stakeholders; keep all parties informed of quality progress, risks, and decisions at portfolio level\nCascade quality strategy and engineering standards across Software Development; ensure alignment between quality direction and business goals\nRepresent Software Development across Lhasa and with senior stakeholders where required\nAct as a key quality contact for all delivery team members, including those external to Software Development\nActively contribute to the Software Development community of practice\nCulture and Continuous Improvement\nLead by example as the role model for AI-first quality engineering across the function; champion Lhasa’s values\nDesign innovative AI-human quality collaboration models; identify opportunities for expanded AI testing capability; keep current with advances in AI testing technologies\nBe accountable for the quality excellence of solutions from design through to production operations\nLive the cultural values of Ownership, Integrity, Collaboration, Diversity & Inclusivity, and Curiosity & Adaptability\nUndertake additional tasks and responsibilities which may be reasonably expected of the role\nPromote the visibility of Lhasa within professional networks and at external events\nAdditional responsibilities\nThe following responsibilities may apply based on business need and individuals’ aspirations, skills, and suitability:\nPeople leadership responsibilities may apply based on business need and individual aspiration.\nUndertaking additional tasks to achieve the outcomes of the Software Development function may apply based on business need.\nAbout You\nAI-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.\nEducation and Qualifications\nEssential\nDegree level or equivalent in Computer Science, Mathematics, Chemistry, or a related discipline\nExceptional commercial track record of leading quality engineering at system or portfolio level in lieu of degree\nDesirable\nProfessional certification in software testing (e.g. ISTQB Advanced or Expert)\nSkills, Knowledge and Experience\nEssential\nProven ability to design AI test orchestration workflows, AI harnesses, and validate AI-generated quality architectures at portfolio level\nDeep expertise in AI test orchestration platforms, multi-agent test system architecture, test context engineering, AI harnesses, and the capabilities and limitations of AI testing\nDeep and extensive commercial experience in quality engineering, with a proven track record of technical leadership and strategic influence\nStrong knowledge of test architecture patterns and quality systems thinking at portfolio level\nPractical experience designing, writing, and maintaining automated tests across the full stack, the craft that underpins effective AI direction and validation\nExperience designing and delivering quality assurance for distributed systems or enterprise-scale applications\nExperience of non-functional testing (performance, security, accessibility, reliability) at enterprise scale\nExperience designing AI-augmented Agile quality workflows and AI quality governance frameworks\nExperience 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\nWorking knowledge of AI coding/testing assistants, AI code-review tooling, and multi-agent orchestration frameworks; keeping current with this fast-evolving landscape is expected\nExperience of building testing frameworks and quality gates for AI-generated code at portfolio level\nExperience of acting as a quality engineering lead of a solutions team\nDesirable\nExperience of testing software products in a scientific domain\nExperience of performance and line management in a matrix environment\nInterested? Apply now\n\"*\" indicates required fields\nEqual opportunities statement\nLhasa Limited aims to ensure that no job applicant, colleague, or worker receives less favourable treatment on the grounds of race, colour, caste, national or ethnic origins, sex, pregnancy or pregnancy related reasons, marital or civil partnership status, sexual orientation, disability, membership or non-membership of trade union, “spent convictions” of ex-offenders, class, age, political or religious belief.\nLhasa will make reasonable adjustments for all candidates who have declared that they have a disability at all stages in the recruitment process. 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