Lead the implementation and operationalisation of knowledge base and retrieval-augmented generation solution stacks to support scalable GenAI and agentic use cases across business domains
AI Service Enablement: Enable enterprise-wide adoption of AI capabilities by developing reusable APIs, SDKs, templates and shared services that simplify AI integration and accelerate solution delivery across teams, while maintaining AI security and compliance within the organisation.
You will work across the entire stack — from frontend experiences and APIs to distributed systems, data infrastructure, LLM orchestration, agentic workflows, evaluation systems, and autonomous optimization loops.
Collaborate with application teams, architects, and security specialists to ensure smooth delivery and operation of cloud-native and container-based applications.
Build and lead a small engineering team, including hiring, onboarding, coaching and developing engineers, setting clear expectations and fostering a culture of ownership, technical excellence and continuous learning.
Evaluate inference engines (vLLM, TensorRT-LLM, Triton Inference Server) and publish recommendations balancing performance, cost, and resilience. Provide hands-on support to agency teams applying these to their workloads.
Reporting directly to the AI Architect, the Staff AI Systems Engineer will serve as a key technical partner in translating AI product requirements and architectural vision into deployable AI solutions.
Evaluate inference engines (vLLM, TensorRT-LLM, Triton Inference Server) and publish recommendations balancing performance, cost, and resilience. Provide hands-on support to agency teams applying these to their workloads.
You will lead the cloud/edge cloud infrastructure projects you are responsible for delivering. Your focus is to turn business and technical requirements into deliverable plans, hold suppliers accountable, coordinate data centre readiness, and ensure that new capacity is accepted and handed over to operations with clear ownership.
Set standards for data product design, data contracts, semantic consistency, trusted context, privacy/security-by-design, and measurable business value.