Job description
Role Overview
Data Intelligence MLOps Engineer at Dyson. The role is based within Dyson's new Data Intelligence team, which sits at the heart of the company's mission to drive innovation in engineering, AI, and robotics.
Company Overview
Dyson is driven by a relentless pursuit of innovation, pushing boundaries in engineering, AI, and robotics. The new Data Intelligence team shapes Dyson's future through data, blending creativity, precision, and audacity to power intelligent products. The team crafts data strategies and pipelines that fuel the next generation of connected devices and works alongside brilliant minds from Dyson's global engineering team and external software and hardware partners in an environment built for exploration, discovery, delivery, and impact.
Role Purpose
Design, build, and maintain the backbone of Dyson's Machine Learning lifecycle. You will be responsible for the industrialization of AI, moving models from experimental notebooks into robust, production-grade pipelines. Your mission is to automate the journey from raw data curation to model deployment, ensuring CI/CD cycles are fast, observable, and reproducible.
Key Responsibilities
Pipeline Orchestration & Automation
- Build and manage automated workflows for data preparation, feature engineering, model training, and evaluation.
Machine Learning CI/CD & Continuous Training
- Develop Continuous Integration systems for code testing.
- Develop Continuous Deployment systems for model serving.
- Develop Continuous Training systems with automated retraining triggers.
Infrastructure & Scalability
- Manage scalable Machine Learning infrastructure using Infrastructure as Code (IaC) tools such as MLFlow.
- Manage Docker and Kubernetes environments for distributed training and inference.
Monitoring & Observability
- Implement dashboards and alerts for model drift, data skew, and system performance including latency and throughput.
Registry & Versioning
- Maintain the Model Registry and Feature Store to ensure versioning and lineage across all experiments.
Security & Compliance
- Ensure data privacy and secure access controls throughout the ML lifecycle.
Qualifications & Experience
- 3 or more years in DevOps, Data Engineering, or MLOps roles.
- Proven track record of taking at least one ML project from a research phase to a high-availability production environment.
- Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related technical field.
Skills & Competencies
- Expertise in orchestration tools: Kubeflow, Airflow, Dagster, or Prefect.
- Mastery of Docker and Kubernetes (K8s) for managing distributed training and inference.
- Deep experience with cloud platforms: AWS (SageMaker), GCP (Vertex AI), or Azure ML.
- Advanced Git workflows and experience with DVC (Data Version Control) or MLflow.
- Experience with CI/CD frameworks: GitHub Actions, GitLab CI, or Jenkins, specifically for ML artifacts.
- High proficiency in Python and Bash for automation.
Additional Information
Dyson is an equal opportunity employer. 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 any other dimension of diversity.