Senior Data Intelligence MLOps Engineer

Overview

About The Role

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.
• Dyson is an equal opportunity employer.
• We know that great minds don’t think alike, and it takes all kinds of minds to make our technology so unique.
• We welcome applications from all backgrounds and 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 other any other dimension of diversity.