Machine Learning Engineer - Stott and May

MLOps Engineer Location: London, UK (Hybrid – 2 days per week in office) Day Rate: Market rate (Inside IR35 Duration: 6 months Role Overview As an MLOps Engineer, you will support machine learning products from inception, working across the full data ecosystem. This includes developing application-specific data pipelines, building CI/CD pipelines that automate ML model training and deployment, publishing model results for downstream consumption, and building APIs to serve model outputs on-demand. The role requires close collaboration with data scientists and other stakeholders to ensure ML models are production-ready, performant, secure, and compliant. Key Responsibilities • Design, implement, and maintain scalable ML model deployment pipelines (CI/CD for ML) • Build infrastructure to monitor model performance, data drift, and other key metrics in production • Develop and maintain tools for model versioning, reproducibility, and experiment tracking • Optimize model serving infrastructure for latency, scalability, and cost • Automate the end-to-end ML workflow, from data ingestion to model training, testing, deployment, and monitoring • Collaborate with data scientists to ensure models are production-ready • Implement security, compliance, and governance practices for ML systems • Support troubleshooting and incident response for deployed ML systems Required Skills And Experience • Strong programming skills in Python; experience with ML libraries such as Snowpark, PySpark, or PyTorch • Experience with containerization tools like Docker and orchestration tools like Airflow or Astronomer • Familiarity with cloud platforms (AWS, Azure) and ML services (e.g., SageMaker, Vertex AI) • Experience with CI/CD pipelines and automation tools such as GitHub Actions • Understanding of monitoring and logging tools (e.g., NewRelic, Grafana) Desirable Skills And Experience • Prior experience deploying ML models in production environments • Knowledge of infrastructure-as-code tools like Terraform or CloudFormation • Familiarity with model interpretability and responsible AI practices • Experience with feature stores and model registries #Jobster