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Senior Data Intelligence MLOps Engineer
Senior Data Intelligence MLOps Engineer
As a Senior Data Intelligence MLOps Engineer, you will be responsible for designing, building, and maintaining the Machine Learning lifecycle at Dyson. Your primary mission is to automate the journey from data curation to model deployment, ensuring robust, production-grade pipelines and fast, observable CI/CD cycles. You will industrialize AI by moving models from experimental stages to high-availability production environments.
About the role
About The Role
We are seeking a Senior Data Intelligence MLOps Engineer to design, build, and maintain the backbone of our 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 our CI/CD cycles are fast, observable, and reproducible.
Key Responsibilities
- End-to-End Pipeline Orchestration: Build and manage automated workflows for data preparation, feature engineering, model training, and evaluation.
- Machine Learning CI/CD/CT Implementation: Develop Continuous Integration (code testing), Continuous Deployment (model serving), and Continuous Training (retraining triggers) systems.
- Infrastructure as Code (IaC): Manage scalable Machine Learning infrastructure using tools like MLFlow
- Model Monitoring & Observability: Implement dashboards and alerts for model drift, data skew, and system performance (latency/throughput).
- Registry Management: 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.
About you
- 5+ 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.
- Orchestration Tools: Expertise in Kubeflow, Airflow, Dagster, or Prefect.
- Containerization: Mastery of Docker and Kubernetes (K8s) for managing distributed training and inference.
- Cloud Platforms: Deep experience with AWS (SageMaker), GCP (Vertex AI), or Azure ML.
- Version Control: Advanced Git workflows and experience with DVC (Data Version Control) or MLflow.
- CI/CD Frameworks: Experience with GitHub Actions, GitLab CI, or Jenkins specifically for ML artifacts.
- Scripting: High proficiency in Python and Bash for automation.