Senior MLOps Engineer in London

Company Description

Who is Hello Kindred?

Hello Kindred are specialists in staffing marketing, creative and technology roles, offering a range of talent solutions that can be delivered on-site, remotely or hybrid.

Our vision is to make work accessible and people’s lives better.

We do this by disrupting traditional employment barriers – connecting ambitious talent to flexible opportunities with trusted brands.

Job Description

Anticipated Contract End Date/Length

Approximately 5 months

Work Set Up

On-site location options available in London or Horsham

Clearance Required

• BPSS
• Our client in the Information Technology and Services industry is looking for a

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.

What you will do

• Partner with Solution and Enterprise Architects to design and implement end-to-end MLOps solutions on Azure.
• Build and operate scalable machine learning 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 Git Ops practices.
• Implement monitoring and observability for model performance, model drift, application performance, platform health, infrastructure, and operational metrics.
• Leverage Azure Machine Learning, AKS, Azure Monitor, Application Insights, and Azure Dev Ops or Git Hub Actions.
• Optimise cloud infrastructure utilisation and spend across ML workloads.
• Implement efficient compute and scaling strategies across training and inference environments.
• Drive Fin Ops practices, cost visibility, and resource right-sizing.
• Improve platform performance, reliability, throughput, and latency.
• Collaborate with technical stakeholders to support scalable, reliable, and cost-efficient MLOps operations.

Qualifications

• Hold 8+ years of experience in Software Engineering, Platform Engineering, Dev Ops, or MLOps.
• Have 5+ years of experience building and operating production MLOps platforms.
• Possess strong hands-on experience with Azure-based MLOps architectures and AKS.
• Demonstrate deep expertise in Kubernetes, containerisation, and model deployment patterns.
• Apply experience implementing monitoring, observability, and model lifecycle management.
• Apply hands-on experience with CI/CD pipelines and Infrastructure as Code.
• Have experience with Azure Monitor, Application Insights, Azure Dev Ops, and/or Git Hub Actions.
• Demonstrate proficiency with Terraform, Bicep, or equivalent Infrastructure as Code tooling.
• Possess strong Python and scripting skills.
• Have experience supporting low-latency ML inference workloads and cloud cost optimisation initiatives.
• Meet eligibility requirements for BPSS clearance.
• Work through an umbrella company on a PAYE basis.
• Additional Information

Candidates must be legally authorized to live and work in the country where the position is based, without requiring employer sponsorship.

Hello Kindred is committed to fair, transparent, and inclusive hiring practices. We assess candidates based on skills, experience, and role-related requirements.

We appreciate your interest in this opportunity. While we review every application carefully, only candidates selected for an interview will be contacted.

Hello Kindred is an equal opportunity employer.

We welcome applicants of all backgrounds and do not discriminate on the basis of race, colour, religion, sex, gender identity or expression, sexual orientation, age, national origin, disability, veteran status, or any other protected characteristic under applicable law.