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Role Overview
Senior Data Intelligence MLOps Engineer at Dyson, based on location as specified by the employer.
Company Overview
Dyson is driven by relentless pursuit of innovation in engineering, AI, and robotics. The Data Intelligence team sits at the heart of this mission, shaping Dyson's future through data by 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 the Machine Learning lifecycle. Move models from experimental notebooks into robust, production-grade pipelines. Automate the journey from raw data curation to model deployment, ensuring CI/CD cycles are fast, observable, and reproducible.
Key Responsibilities
Pipeline Orchestration
- Build and manage automated workflows for data preparation, feature engineering, model training, and evaluation.
Machine Learning CI/CD Implementation
- Develop Continuous Integration systems for code testing.
- Develop Continuous Deployment systems for model serving.
- Develop Continuous Training systems for retraining triggers.
Infrastructure Management
- Manage scalable Machine Learning infrastructure using tools like MLFlow.
- Manage infrastructure as code across the ML lifecycle.
Model Monitoring & Observability
- Implement dashboards and alerts for model drift.
- Implement dashboards and alerts for data skew.
- Implement dashboards and alerts for system performance including latency and throughput.
Registry & Versioning
- Maintain the Model Registry and Feature Store.
- Ensure versioning and lineage across all experiments.
Security & Compliance
- Ensure data privacy throughout the ML lifecycle.
- Secure access controls throughout the ML lifecycle.
Qualifications & Experience
- 5 or more years in DevOps, Data Engineering, or MLOps roles.
- Proven track record of taking at least one ML project from research phase to high-availability production environment.
- Bachelor's degree 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 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 and welcomes applications from all backgrounds.
- 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.