At a Glance
- Tasks: Manage and enhance Databricks environments for ML Engineering and MLOps.
- Company: Leading organisation at the forefront of data science innovation.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Other info: Collaborative culture with a focus on career development and impactful projects.
- Why this job: Join a dynamic team and shape the future of machine learning deployment.
- Qualifications: Strong experience with Databricks and MLOps in production environments.
The predicted salary is between 70000 - 90000 £ per year.
A leading organisation is looking for a Databricks-focused MLOps Engineer to take ownership of a dedicated ML Engineering environment, supporting a growing data science team and accelerating the route from model development into production. The business operates across a modern data landscape including both Palantir and Databricks, with some cross-platform integration expected as the environment develops. This role will focus specifically on the Databricks MLOps setup, ensuring it is performant, scalable, secure, well-governed, and able to support production ML products in a structured and repeatable way.
You will manage and improve the Databricks environments used by a team of 8 data scientists, with the team growing quickly as demand for ML products increases. The main focus is improving how models are deployed, monitored, governed, and supported in production. This is a delivery-focused role with a strategic element. The client needs someone who can understand the Databricks roadmap, advise on what the business should adopt, and turn that into practical MLOps frameworks, deployment patterns, and operating processes. You will help bring the target operating model to life, create a clear path-to-production, and support the internal ML Engineering capability while the permanent team continues to grow.
Key Responsibilities
- Own and manage dedicated Databricks environments supporting ML Engineering and MLOps
- Ensure the platform is performant, scalable, secure, and well-governed
- Support a growing team of data scientists in operationalising, deploying, and managing their models
- Build out reusable MLOps frameworks, standards, and deployment patterns
- Improve the path from model development through to production
- Support model observability, monitoring, governance, and operational controls
- Work closely with Databricks to understand their roadmap and advise on relevant adoption
- Help bring the full MLOps operating model and solution design to life
- Support the development of internal ML Engineering capability
- Work across Databricks, Palantir, data science, and engineering teams where required
- Ensure ML products and services can be delivered in a structured, repeatable, and scalable way
Key Skills and Experience
- Strong experience with Databricks in a production ML, MLOps, or data platform environment
- Experience working across MLOps, ML Engineering, or ML Platform Engineering
- Strong understanding of model deployment, model monitoring, CI/CD, versioning, and ML lifecycle management
- Experience building frameworks, standards, and reusable patterns for production ML delivery
- Experience supporting data scientists and helping move models into production
- Strong Python and PySpark experience
- Experience with cloud data platforms, ideally Azure
- Strong understanding of scalable and governed ML platform environments
- Ability to operate strategically while remaining hands-on and delivery-focused
- Strong stakeholder management skills across technical and non-technical teams
Nice to Have
- Palantir experience or exposure to cross-platform data environments
- Unity Catalog, Delta Lake, MLflow, Feature Store, or Model Registry experience
- Experience building out ML Engineering capability or MLOps functions
- Experience in enterprise or regulated environments
- Vendor roadmap or platform strategy experience
- Responsible AI, model governance, or risk management experience
- Cloud certifications or Databricks certifications
Senior MLOps Engineer, Databricks in London employer: Harnham
Join a forward-thinking organisation that prioritises innovation and collaboration, offering a dynamic work environment for a Senior MLOps Engineer focused on Databricks. With a commitment to employee growth, you will have the opportunity to shape the future of ML Engineering while working alongside a talented team of data scientists in a supportive culture that values strategic input and hands-on delivery. Located in a vibrant area, the company provides access to cutting-edge technology and resources, ensuring you can thrive in your role and make a meaningful impact.
StudySmarter Expert Advice🤫
We think this is how you could land Senior MLOps Engineer, Databricks in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those already working with Databricks or MLOps. Attend meetups, webinars, or even just grab a coffee with someone who’s in the know. You never know where a casual chat might lead!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your MLOps projects, especially any work with Databricks. This could be a GitHub repo or a personal website. It’s a great way to demonstrate your hands-on experience and make you stand out.
✨Tip Number 3
Prepare for interviews by diving deep into the specifics of Databricks and MLOps. Brush up on your knowledge of model deployment, monitoring, and CI/CD processes. Being able to discuss these topics confidently will show you’re ready to hit the ground running.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got loads of opportunities that might just be the perfect fit for you. Plus, it’s a great way to get noticed by our hiring team directly. Let’s get you that dream job!
We think you need these skills to ace Senior MLOps Engineer, Databricks in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Senior MLOps Engineer role. Highlight your experience with Databricks, MLOps, and any relevant frameworks you've built. We want to see how your skills align with what we're looking for!
Showcase Your Projects:Include specific projects where you've improved model deployment or monitoring. We love seeing real-world examples of your work, especially if they relate to the responsibilities mentioned in the job description.
Be Clear and Concise:When writing your application, keep it clear and to the point. Use bullet points for key achievements and avoid jargon unless it's relevant. We appreciate straightforward communication that gets to the heart of your experience.
Apply Through Our Website:Don't forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you're keen on joining our team at StudySmarter!
How to prepare for a job interview at Harnham
✨Know Your Databricks Inside Out
Make sure you’re well-versed in Databricks and its MLOps capabilities. Brush up on the latest features, best practices, and how they can be applied to improve model deployment and monitoring. Being able to discuss specific examples of how you've used Databricks in previous roles will definitely impress.
✨Showcase Your MLOps Frameworks
Prepare to talk about the MLOps frameworks and standards you've built or worked with. Be ready to explain how these frameworks improved the path from model development to production. Highlight any reusable patterns you've created that could benefit the team you're interviewing for.
✨Demonstrate Stakeholder Management Skills
Since this role involves working with both technical and non-technical teams, think of examples where you've successfully managed stakeholder expectations. Discuss how you’ve communicated complex technical concepts to non-technical audiences, as this will show your ability to bridge gaps between teams.
✨Be Hands-On and Strategic
This position requires a balance of strategic thinking and hands-on execution. Prepare to discuss how you've operated in similar roles, focusing on how you’ve contributed to both the strategic direction and the day-to-day operations. Share specific instances where your hands-on approach led to successful outcomes.
