Crew Tech Lead

Provide architectural leadership across cloud-native, distributed and microservices-based platforms, including API-led and event-driven architectures.

Drive appropriate decomposition of legacy and monolithic platforms into reusable business capabilities and independently scalable services, while avoiding unnecessary architectural complexity.

Lead the responsible adoption of AI and agentic engineering, with demonstrated experience taking AI-enabled capabilities beyond experimentation into measurable business or customer outcomes.

Establish engineering patterns for integrating AI models, agents, enterprise APIs, knowledge and data while ensuring appropriate security, governance, observability and human oversight.

Champion modern software engineering practices across automated testing, CI/CD, DevSecOps, SRE, observability and platform engineering.

Build strong partnerships across Product, Operations, Architecture, Data, AI, Risk and other Technology teams to translate business strategy into executable technology outcomes.

Drive simplification and reuse, reducing dependency on bespoke and legacy technologies in favour of strategic enterprise platforms and reusable capabilities.

Maintain strong accountability for the resilience, security, risk and operational performance of production services owned by the crew.

We're interested in hearing from people who have:

Proven experience leading, motivating and developing large engineering organisations of ~ 70+ people, including Engineering Managers and senior technical leaders, within a complex enterprise environment.

A demonstrated track record of building high-performing engineering cultures, developing talent, raising technical standards and creating strong ownership across geographically distributed teams.

Strong software engineering foundations with sufficient hands-on technical depth to challenge designs, influence architecture and provide credibility with senior engineers.

Hands-on engineering experience in one or more technologies such as Java,.NET, Python, React or similar modern technology stacks.

Strong experience designing and operating cloud-native microservices and distributed architectures, including APIs, event-driven integration, containerisation, resilience patterns and observability.

Demonstrated experience delivering AI/GenAI initiatives into real business environments, ideally including agentic AI, LLM-based applications, RAG/knowledge architectures, AI evaluation or MLOps.

An understanding of how to safely operationalise AI, including responsible AI, security, model and agent evaluation, data governance, human-in-the-loop controls and production observability.

Strong experience with modern cloud engineering practices (AWS is added advantage)