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