Competitive, depending on experience
Brazil (remote)
The Principal AI Engineer provides strategic AI-first engineering leadership for Lhasa Software Development at portfolio level. As the technical lead of a solutions team, they own engineering standards, AI governance, and AI-human collaboration patterns across Lhasa, and design and govern the multi-agent workflows that deliver consistent, high-quality software at scale. The Principal AI Engineer sets the standard for how AI becomes the primary mechanism for achieving them, with human engineering judgement and authorship applied where the work demands it.
The Principal AI Engineer sets the standard for AI-first software engineering across the function, brings deep engineering judgement across architecture, distributed systems, performance, and security, mentors the wider Software Development team, and contributes to the technical roadmap and enterprise AI strategy.
- Set the architectural guardrails, technical standards, AI harness patterns, and acceptance frameworks for AI agents portfolio-wide
- Design and govern multi-agent workflows where specialist AI agents handle design, implementation, and testing under appropriate human oversight; define requirements, acceptance criteria, and quality gates
- Own the design and governance of automated security and quality gates in the development pipeline; ensure security-by-design and quality-by-design are embedded from the point of AI code generation
- Validate AI-generated code, architecture, and tests at portfolio level; intervene directly on complex business logic, distributed-system concerns, performance, and security
- Manage AI reliability and hallucination risks at scale; design appropriate human-in-the-loop checkpoints for situations where AI delivery alone is insufficient
- Direct AI coding agents and harnesses to design, implement, test, and support software across the Life Sciences domain; apply deep domain knowledge to validate the result
- Carry out hand-written code, debugging, and design work where AI tooling is unlikely to produce the right result, or where the engineering risk warrants direct human authorship
- Embed engineering quality early and continuously across multiple products
- Take ownership of complex engineering problems across the portfolio and ensure they are successfully resolved
- Lead Technical Excellence strategy, continuous improvement, and outcomes across Lhasa Software Development
- Ensure that software products satisfy architectural characteristics including performance, data confidentiality, resilience, scalability, and security across the portfolio
- Define decision criteria and technical constraints; use AI-generated evidence to inform strategic technical decisions in collaboration with Product Owners, Delivery Managers, and other Principal Engineers
- Maintain a deep understanding of AI capabilities and limitations; design AI-human collaboration models that balance automation with human judgement at portfolio scale
- Define AI agent capability requirements and orchestration frameworks; own the technical roadmap contribution for Software Development; collaborate on enterprise AI governance
- Own context engineering standards, AI harnesses, and governance across Lhasa; ensure engineering knowledge is embedded in agent infrastructure and not dependent on any single individual
- Provide expert technical input to portfolio-level AI compute cost decisions in collaboration with Engineering Management
- Produce architectural design documentation for product solutions; lead the development and iteration of engineering standards in collaboration with senior colleagues
- Serve as the technical lead for a solutions team, providing direction, setting standards, and being accountable for technical outcomes across the portfolio
- Develop the engineering capability of the Software Development function for AI agent orchestration; build AI-human collaboration skills across teams; focus mentorship on outcome definition, engineering judgement, and AI-limitation awareness
- Pioneer and champion best practices observed within Lhasa and externally; raise the engineering bar across the function
- Identify engineering gaps across the portfolio and own the plans to address them
- Collaborate with the Architecture Team and contribute to product and portfolio architecture
- Technically lead the software engineering discipline within Lhasa alongside other Technical Leaders
- Communicate clearly with delivery teams and senior stakeholders; keep all parties informed of technical progress, risks, and decisions at portfolio level
- Cascade technical strategy and engineering standards across Software Development; ensure alignment between technical direction and business goals
- Represent Software Development across Lhasa and with senior stakeholders where required
- Act as a key technical 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 software engineering across the function; champion Lhasa’s values
- Design innovative AI-human collaboration models; identify opportunities for expanded AI capability; keep current with advances in AI technologies and orchestration
- Be accountable for the technical 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, engineering-minded, and strategic. Collaborative, professional, and supportive. Takes full accountability for technical outcomes at portfolio scale, thinks beyond the immediate task to the broader technical and business outcome, and continuously seeks better ways to use AI to accelerate and improve delivery. Models openness to change at a strategic level and shapes 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 technical delivery at system or portfolio level in lieu of degree
- Proven ability to design AI agent orchestration workflows, AI harnesses, and validate AI-generated architectural designs at portfolio level
- Deep expertise in AI agent orchestration platforms, multi-agent system architecture, context engineering, AI harnesses, and the capabilities and limitations of AI
- Deep and extensive commercial experience in software engineering, with a proven track record of technical leadership and strategic influence
- Strong knowledge of software architecture patterns and systems thinking at portfolio level
- Practical experience designing, writing, debugging, and maintaining production code, the craft that underpins effective AI direction and validation
- Experience of designing and delivering distributed systems or enterprise-scale applications
- Experience of non-functional engineering at enterprise scale including performance, security, resilience, and scalability, and an ability to balance these against delivery pressure
- Experience designing AI-augmented Agile delivery workflows and AI 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 assistants, AI code-review tooling, and multi-agent orchestration frameworks; keeping current with this fast-evolving landscape is expected
- Experience of quality gates for AI-generated code and automated validation tooling at portfolio level
- Experience of acting as a technical lead of a solutions team
- Experience with microservices, multithreading, and performance profiling
- Experience of developing software products in a scientific domain
- Experience of performance and line management in a matrix environment
