Data Intelligence MLOps Engineer

About Dyson and the Data Intelligence Team

Dyson thrives on innovation in engineering, AI, and robotics. Our Data Intelligence team is central to driving these advances by developing data strategies and pipelines that support the next wave of smart, connected products. This team collaborates with Dyson's global engineering units as well as external software and hardware partners, fostering a culture focused on experimentation, discovery, delivery, and impactful results.

Role Overview

We are looking for a Data Intelligence MLOps Engineer to architect, construct, and sustain the core infrastructure powering our Machine Learning lifecycle. The engineer will transform AI models from experimental stages into robust, scalable production pipelines. The goal is to automate the entire process—from raw data preparation through to model deployment—ensuring the deployment cycles are efficient, transparent, and reproducible.

Key Responsibilities

• Design and operate end-to-end automated pipelines handling data preparation, feature extraction, model training, and performance evaluation.
• Implement Continuous Integration, Continuous Deployment, and Continuous Training frameworks specifically tailored for ML workflows.
• Leverage Infrastructure as Code principles to manage scalable ML infrastructure, utilizing tools like MLFlow.
• Develop systems for monitoring models and infrastructure, including dashboards and alerts covering model drift, data anomalies, and performance metrics such as latency and throughput.
• Oversee Model Registry and Feature Store management to maintain version control and traceability of experiments and features.
• Ensure security and compliance throughout the ML lifecycle, including data privacy and controlled access.

Candidate Profile

• Minimum 3 years experience in DevOps, Data Engineering, or MLOps-focused roles.
• Track record of successfully transitioning at least one ML project from prototype to a stable, production-grade environment.
• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or related field.
• Experienced with orchestration tools such as Kubeflow, Apache Airflow, Dagster, or Prefect.
• Proficiency in containerization technologies including Docker and Kubernetes for distributed training and inference.
• Hands-on experience with cloud ML platforms like AWS SageMaker, Google Cloud Vertex AI, or Azure ML.
• Advanced knowledge of version control systems, including Git workflows and tools like DVC or MLflow.
• Familiarity with CI/CD systems such as GitHub Actions, GitLab CI, or Jenkins for deploying ML artifacts.
• Strong scripting capabilities using Python and Bash to automate workflows.

Diversity and Inclusion

Dyson upholds equal opportunity employment practices, welcoming candidates from diverse backgrounds and ensuring recruitment and employment decisions are free from discrimination based on identity, experience, or any other protected characteristic.