MLOps Engineer

Stott and May; Greater London; Hybrid; GBP 80,000 - 100,000; Full time

Job summary

A technology recruitment company is seeking an MLOps Engineer in London, UK. The position involves supporting machine learning products by developing scalable ML deployment pipelines and ensuring models are production-ready. Candidates must have strong Python programming skills and experience with ML libraries, containerization tools, and cloud platforms. This is a hybrid role with a focus on collaboration and automation in the ML workflow.

Qualifications

• Experience deploying ML models in production environments preferred.
• Knowledge of infrastructure-as-code tools like Terraform or CloudFormation is a plus.
• Familiarity with model interpretability and responsible AI practices is desirable.

Responsibilities

• Design, implement, and maintain ML model deployment pipelines.
• Build infrastructure to monitor model performance and data drift.
• Develop tools for model versioning, reproducibility, and experiment tracking.
• Automate the end-to-end ML workflow from data ingestion to deployment.

Skills

Strong programming skills in Python

Experience with ML libraries like Snowpark, PySpark, or PyTorch

Experience with containerization tools like Docker

Experience with orchestration tools like Airflow or Astronomer

Familiarity with cloud platforms (AWS, Azure)

Experience with CI/CD pipelines

Understanding of monitoring and logging tools

Job description

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