Machine Learning Engineer SRE

Job Summary

We are seeking an experienced Machine Learning Engineer with a strong Site Reliability Engineering (SRE) and MLOps mindset to support the deployment, operation and reliability of machine-learning applications in production environments.

The successful candidate will have hands-on experience supporting applications across Linux and Windows environments, managing on-premises infrastructure, operating Kubernetes and Docker-based workloads, and developing automation and troubleshooting solutions using Python.

This position sits at the intersection of Machine Learning, MLOps, DevOps and Site Reliability Engineering. The engineer will collaborate closely with Data Scientists, Software Engineers and infrastructure teams to ensure ML applications and models can be deployed, monitored and operated reliably at scale.

Key Responsibilities

• Deploy, maintain and support machine-learning applications across Windows and Linux production environments.
• Manage, monitor and troubleshoot Kubernetes clusters and Docker containers supporting ML workloads.
• Work with Data Scientists and Engineering teams to operationalise and deploy ML models into production.
• Support the complete lifecycle of machine-learning models, including deployment, monitoring, troubleshooting and ongoing maintenance.
• Develop Python-based scripts and automation to improve operational efficiency and system reliability.
• Implement and maintain monitoring and alerting capabilities using DataDog.
• Troubleshoot complex application, infrastructure and distributed-system issues in production environments.
• Build and maintain CI/CD pipelines supporting ML application and model deployments.
• Support on-premises servers and infrastructure used by ML applications.
• Apply SRE and DevOps practices to improve system reliability, availability, scalability and performance.
• Follow security and operational best practices across production environments.
• Maintain clear documentation covering architecture, deployment processes, operational procedures and troubleshooting guides.
• Collaborate effectively across Machine Learning, Engineering, DevOps and infrastructure teams.

Required Skills & Qualifications

• Strong hands-on experience supporting applications in Linux and Windows production environments.
• Experience managing Kubernetes clusters and Docker/containerised applications.
• Strong programming and scripting experience using Python.
• Good understanding of fundamental machine-learning concepts and workflows.
• Hands-on experience with ML model deployment and lifecycle management.
• Experience supporting and troubleshooting ML applications in production.
• Knowledge of monitoring and observability platforms, preferably DataDog.
• Strong production troubleshooting and debugging skills.
• Experience implementing or supporting CI/CD pipelines.
• Familiarity with AWS environments and services.
• Understanding of Site Reliability Engineering, DevOps and automation practices.
• Ability to troubleshoot issues across distributed systems and application infrastructure.
• Strong problem-solving, documentation, communication and collaboration skills.