Data Intelligence MLOps Engineer

About Dyson and the Team

Dyson is fueled by innovation, constantly advancing in engineering, artificial intelligence, and robotics. Our Data Intelligence team plays a crucial role in driving Dyson's future by designing cutting-edge data strategies and pipelines that empower the latest connected products. This team collaborates with expert engineers globally and partners externally to foster an environment of creativity, exploration, and impactful delivery.

Role Overview

We are recruiting a Data Intelligence MLOps Engineer responsible for architecting, developing, and sustaining the core infrastructure underpinning our machine learning lifecycle. The role centers on transitioning AI models from experimental stages into production-ready, reliable systems. Key duties include automating workflows that handle the entire process from data curation to deployment while ensuring fast, transparent, and repeatable continuous integration and deployment cycles.

Key Responsibilities

• Designing and managing end-to-end automated pipelines encompassing data processing, feature generation, model training, and evaluation.
• Implementing CI/CD and continuous training systems tailored for machine learning, covering code validation, model deployment, and retraining triggers.
• Utilizing Infrastructure as Code (IaC) tools to maintain scalable ML infrastructure, including platforms like MLFlow.
• Establishing monitoring solutions and observability tools with dashboards and alerts to detect model drift, deviations in data, and performance issues such as latency or throughput bottlenecks.
• Overseeing the Model Registry and Feature Store to guarantee version control and experiment traceability.
• Ensuring security compliance by safeguarding data privacy and implementing secure access mechanisms throughout the ML workflow.

Candidate Profile

• Minimum of three years’ experience in DevOps, Data Engineering, or MLOps environments.
• Demonstrable success in advancing at least one machine learning initiative from research to robust, high-availability production service.
• Educational qualifications including a Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related fields.
• Proficient in orchestration frameworks such as Kubeflow, Apache Airflow, Dagster, or Prefect.
• Strong command of container technologies including Docker and Kubernetes for managing distributed workloads in training and inference.
• Hands-on experience with major cloud ML platforms like AWS SageMaker, Google Cloud Vertex AI, or Azure Machine Learning.
• Advanced capabilities in version controlling ML assets using Git and tools like DVC or MLflow.
• Familiarity with CI/CD pipelines employing GitHub Actions, GitLab CI, or Jenkins focused on ML artifacts.
• Expert scripting skills in Python and Bash for automation purposes.

Diversity and Inclusion

Dyson is committed to equal opportunity employment, valuing diversity and inclusion. The company encourages candidates from all backgrounds to apply and makes hiring decisions free from biases related to race, ethnicity, gender identity, age, disability, or any other characteristic.