Skills

Python Incident Response GitHub CI/CD Docker Monitoring Training Machine Learning PyTorch Programming Azure AWS cloud platforms CI/CD Pipelines PySpark Terraform Grafana GitHub Actions

Job Specifications

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

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About the Company

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