Role purpose Lead the design, delivery and operation of robust, production-grade MLOps/AIOps capabilities that move Dyson’s ML, AI and GenAI/agent systems from development into reliable production, and keep them healthy at scale. Own and evolve the engineering patterns for CI/CD, cloud infrastructure, deployment, monitoring and lifecycle management, and set the standards followed across the Data Science and AI estate
Role overview
- Combines hands-on engineering with technical leadership
- Leads deployment and operation of scalable ML and AI/agent systems
- Designs and runs CI/CD pipelines and cloud infrastructure
- Implements monitoring, logging, evaluation and lifecycle management
- Works closely with data science, AI engineering, platform, security and governance teams
- Ensures solutions are reliable, secure and compliant with organisational standards
- Mentors engineers and raises the bar for engineering practices across the team and drives continuous improvement
Key responsibilities
Build & Deploy
- Design, build and maintain CI/CD pipelines for ML, AI and agent systems
- Deploy and operate ML models and AI/agents in production (e.g. Cloud Run, containerised services)
- Develop containerisation and orchestration strategies Platform & Infrastructure
- Architect and manage solutions on cloud infrastructure (GCP) and Infrastructure as Code (Terraform)
- Optimise infrastructure for performance, scalability and cost
- Define and evolve the MLOps/AIOps platform roadmap, aligning with AI, cloud and governance strategies Observability & Quality
- Implement monitoring, logging and observability across performance, latency, errors and drift
- Build and run evaluation pipelines, regression testing and drift detection Lifecycle & Reliability
- Manage model and agent lifecycle (versioning, rollout/rollback, retraining, decommissioning) Own production reliability, incident response and on-call Agent Systems
- Operate AI agent systems, including MCP-based integrations, ensuring observability, evaluation and reliability Cross-team enablement
- Enable multiple teams to adopt standardised deployment, monitoring and lifecycle patterns across ML and AI systems Experience & qualifications Bachelor’s degree in Computer Science, Engineering or related field (Master’s preferred). ~5+ years in MLOps, ML engineering or cloud engineering. Strong experience with Python, Terraform, Docker and Kubernetes, and deep familiarity with GCP and its ML ecosystem.
Dyson is an equal opportunity employer. We know that great minds don’t think alike, and it takes all kinds of minds to make our technology so unique. We welcome applications from all backgrounds and employment decisions are made without regard to race, colour, religion, national or ethnic origin, sex, sexual orientation, gender identity or expression, age, disability, protected veteran status or other any other dimension of diversity.
