Senior MLOps Engineer

We are looking for Senior MLOps Engineer at London, UK – 3 days per week Onsite

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 lifecycle 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

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 lifecycle 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.

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