Responsibilities
- Shape engineering strategy with broad organizational impact — you'll influence long-term architectural direction across multiple teams and products, not just within your own squad.
- Drive platform evolution by identifying cross-cutting pain points and leading the design of secure, scalable, reusable solutions built on AWS and modern serverless and microservice patterns.
- Lead architectural discussions and design reviews where the stakes are real — making clear trade-offs around performance, security, reliability, and maintainability, and getting alignment across teams with competing priorities.
- Own AI architectural decisions end-to-end: design AI-enabled systems with built-in governance, monitoring, and regulatory readiness baked in from the start, not retrofitted. Define where AI adds genuine value and where it doesn't.
- Act as the organization's AI thought leader — educate engineering teams on model behavior, agentic systems, and responsible AI practices, and raise the overall maturity of how we design and deploy AI.
- Mentor and stretch senior and staff engineers, building technical leadership depth across the organisation and holding a high bar for engineering standards.
Requirements
- Principal-level track record at scale. 10+ years of software engineering experience, including at least 3 years' operating at Staff or Principal Engineer level in an organization of 100+ engineers. You've led large, complex platform or AI initiatives where you were the decision-maker, not a contributor.
- Deep, opinionated AWS platform expertise. You've designed multi-account AWS architectures and made the call on when serverless is the wrong choice. You work fluently with Infrastructure as Code — Terraform or CDK preferred — and have strong views on observability, resilience, and security that you've translated into org-wide patterns.
- Production AI systems experience. You've shipped AI-enabled systems — RAG pipelines, agentic frameworks, LLM orchestration, or similar — into production and can discuss the architectural decisions, failure modes, and trade-offs involved. "Understanding AI concepts" is not enough; we need someone who has built and run these systems.
- AI governance in practice. You've defined and implemented AI governance in real production environments — bias and privacy checks in pipelines, audit-ready monitoring, AI usage policies — not just read about it. This is essential, not a bonus.
- API design and backend platform delivery. Proven track record designing and shipping RESTful APIs and backend platform components using Node.js/TypeScript. You understand frontend concerns well enough to set API contracts that serve product teams effectively, even if you're not writing React day-to-day.
- Org-level communication and influence. You can align stakeholders on complex technical decisions, run architecture forums, and mentor other senior technical leaders. You're as comfortable in a board-level conversation as you are in a design review.
Nice to Have
- Experience building platform components or SDKs that multiple product teams depend on, including the documentation and enablement work that makes them adoptable.
- Involvement in architecture forums, tech councils, or similar bodies that define org-wide standards — either running them or playing a leading role.
- Experience optimising cost and performance for data and AI-heavy workloads at scale, including capacity planning and infrastructure tuning.
- Leadership of major modernisation efforts — legacy migration to serverless, multi-region architecture, or platform consolidation across product lines.
- A public technical presence — conference talks, open-source contributions, published writing — that demonstrates thought leadership beyond your current organisation.
