Senior MLOps Engineer

We are a Global Recruitment specialist that provides support to the clients across EMEA, APAC, US and Canada. We have an excellent job opportunity for you.

Role Title: Senior MLOps Engineer
Location: London/Horsham
Duration: 01/02/2027

Role Overview

• We are seeking an experienced Senior MLOps Engineer to support the design, implementation, and optimisation of enterprise-scale MLOps platforms on Microsoft Azure.
• Working closely with Solution and Enterprise Architects, the successful candidate will help build and operate scalable machine learning platforms on Kubernetes, with a focus on model life cycle management, observability, low-latency inference, platform reliability, and cost efficiency.

Key Responsibilities

• Partner with Architects to design and implement end-to-end MLOps solutions on Azure.
• Build and operate scalable ML platforms using Azure Kubernetes Service (AKS) and cloud-native technologies.
• Develop CI/CD and Continuous Training (CT) pipelines for machine learning workloads.
• Deploy, manage, and optimise ML workloads in Kubernetes environments.
• Implement model serving capabilities that meet high-availability and low-latency requirements.
• Configure autoscaling, traffic management, rollback strategies, and resource governance.
• Manage containerised ML applications using Docker, Kubernetes, Helm, and GitOps practices.

Implement monitoring and observability across:

• Model performance and drift
• Application performance and platform health
• Infrastructure and operational metrics
• Leverage Azure services including Azure Machine Learning, AKS, Azure Monitor, Application Insights, and Azure DevOps/GitHub Actions.
• Performance & Cost Optimisation
• Optimise cloud infrastructure utilisation and spend for ML workloads.
• Implement efficient compute and scaling strategies across training and inference environments.
• Drive FinOps practices, cost visibility, and resource right-sizing.
• Improve platform performance, reliability, throughput, and latency.

Required Skills & Experience

• 8+ years' experience in Software Engineering, Platform Engineering, DevOps, or MLOps.
• 5+ years' experience building and operating production MLOps platforms.
• Strong hands-on experience with Azure-based MLOps architectures and AKS.
• Deep expertise in Kubernetes, containerisation, and model deployment patterns.
• Experience implementing monitoring, observability, and model life cycle management.
• Hands-on experience with CI/CD pipelines and Infrastructure as Code.
• Experience with Azure Monitor, Application Insights, Azure DevOps, and/or GitHub Actions.
• Proficiency with Terraform, Bicep, or equivalent.
• Strong Python and Scripting skills.
• Experience supporting low-latency ML inference workloads and cloud cost optimisation initiatives.

If you are interested in this position and would like to learn more, please send through your CV and we will get in touch with you as soon as possible. Please note, candidates are often Shortlisted within 48 hours.