At a Glance
- Tasks: Design and optimise enterprise-scale MLOps platforms on Microsoft Azure.
- Company: Leading IT services firm with a focus on innovation.
- Benefits: Competitive pay, flexible work options, and professional growth opportunities.
- Other info: Exciting projects with potential for career advancement.
- Why this job: Join a dynamic team and shape the future of machine learning operations.
- Qualifications: Experience in MLOps, Kubernetes, and cloud technologies required.
The predicted salary is between 63000 - 77000 £ per year.
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.
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Senior MLOps Engineer employer: Doist
Brink's is an exceptional employer that fosters a collaborative and innovative work culture, empowering employees to drive global commercial strategies in the ATM lifecycle solutions sector. With a strong focus on professional development and growth opportunities, employees are encouraged to expand their skills while contributing to sustainable growth and operational excellence. Located in a dynamic environment, Brink's offers unique advantages such as a diverse team and the chance to build long-term partnerships with global customers, making it a rewarding place to advance your career.
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We think this is how you could land Senior MLOps Engineer
✨Tap into Online Data Science Communities
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Got some cool data science projects? Showcase them on platforms like GitHub or create a personal portfolio website. This visibility is crucial for landing temporary roles—let recruiters see your actual skills in action, which can set you apart from the crowd.
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We think you need these skills to ace Senior MLOps Engineer
Some tips for your application 🫡
Highlight Your Data Projects:When applying for a temporary data science role at Doist, make sure to showcase any relevant projects you've worked on. Whether it's a personal project, an academic undertaking, or contributions to an open-source initiative, detailing these experiences can really set you apart and demonstrate your practical skills.
Emphasise Your Analytical Skills:In your CV and cover letter, focus on the specific analytical skills that are key to data science. Mention any experience with statistical tools, programming languages like Python or R, and data visualisation software. Don't forget to include any certifications that may bolster your expertise!
Show Your Flexibility:Since this is a temporary role, it's important to convey your adaptability and willingness to learn. In your cover letter to Doist, emphasise how quickly you can get up to speed with new tools or projects. Highlight any previous experiences where you've had to adjust to new environments or challenges.
Craft a Unique Data-Driven Cover Letter:Instead of the usual generic cover letter, spice it up with some data! Maybe you’ve improved a process by 20% in a past role or cleaned a dataset with over a million entries. Use these stats to your advantage to grab Doist’s attention and show the tangible impact of your work.
How to prepare for a job interview at Doist
✨Showcase Your Analytical Skills
For a data science gig, it's crucial to demonstrate your analytical abilities. Be ready to discuss previous projects and the methodologies you used. Think about how you can quantify your impact—did your analysis improve efficiency or save costs? These are the stories that will stick with interviewers at Doist.
✨Brush Up on Technical Skills
You might face technical questions on tools relevant to data science, like Python, R, or SQL. Prepare to solve a problem live—perhaps they'll ask you to write a simple query or code snippet. It’s cool to talk about them, but we need to show we can do it in practice, especially in a temporary role where quick results matter.
✨Highlight Your Adaptability
Since this is a temporary position, emphasise your ability to learn quickly and adapt to new tools or workflows. Share examples of how you've thrived in fast-paced environments before, and how you can hit the ground running at Doist.
✨Prepare a Portfolio of Your Work
Bring your portfolio to the table—showcase projects where you've leveraged data science techniques to solve problems. Whether it’s a GitHub repository or a set of case studies, having tangible examples of your work will help you stand out and show what you bring to the team at Doist.
