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Machine Learning Operations (MLOps) Engineer
- oxford, oxfordshire, OX1 1NF, United Kingdom
- Permanent·Hybrid
- Full time
- £78,000 - £78,000 Per Annum
Job Description
About the Role
Our client, a dynamic force in AI and Emerging Technologies , is seeking an experienced Machine Learning Operations (MLOps) Engineer to join their innovative team in Oxford . This role is pivotal in bridging the gap between machine learning model development and reliable production deployment. You will be responsible for building and maintaining the infrastructure and workflows that enable the efficient, scalable, and continuous delivery of machine learning models. This is an excellent opportunity to work with state-of-the-art tools and technologies in a collaborative environment, driving the operational excellence of AI solutions.
Key Responsibilities
- Design, build, and manage scalable MLOps pipelines for model training, deployment, monitoring, and retraining.
- Implement and maintain CI/CD practices for machine learning models.
- Develop and manage infrastructure for ML model serving, ensuring high availability and low latency.
- Monitor ML model performance in production, identify issues, and implement solutions for continuous improvement.
- Collaborate closely with data scientists, software engineers, and DevOps teams to ensure seamless integration and operation of ML systems.
Requirements
- Bachelor's or Master's degree in Computer Science, Engineering, or a related quantitative field.
- Proven experience in MLOps, DevOps, or Software Engineering with a focus on ML systems.
- Proficiency in scripting languages (e.g., Python) and experience with ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
- Hands-on experience with cloud platforms (AWS, Azure, GCP) and containerization technologies (Docker, Kubernetes).
- Strong understanding of CI/CD tools, infrastructure-as-code, and monitoring solutions.
Benefits
- Competitive salary and performance-based incentives.
- Comprehensive health, dental, and vision insurance.
- Opportunities for professional development and advanced training in MLOps.
- Hybrid work model allowing for flexibility in the Oxford area.
- A collaborative and forward-thinking culture focused on innovation and operational excellence.