Lead Mlops Engineer

AI in this role

ai-agentsml-ops

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.

How we rate this

Lead Mlops Engineer at Dyson rates 95 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

AI AgentsML Ops

Questions you could be asked

How do you decide when an AI agent can act on its own versus asking for approval first?
How do you monitor a model once it's live, and how do you know it needs retraining?
How would you decide a model or AI system is ready to ship?
Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

List these exact terms on your resume: AI Agents and ML Ops. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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