At a Glance
- Tasks: Manage and enhance Databricks environments for ML Engineering and MLOps.
- Company: Leading organisation at the forefront of data science and machine learning.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Other info: Collaborative culture with a focus on innovation and career development.
- Why this job: Join a dynamic team and shape the future of ML products in a cutting-edge environment.
- Qualifications: Strong experience with Databricks and MLOps, plus Python and cloud platforms.
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 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 products while working alongside a talented team of data scientists in a supportive culture that values strategic thinking and hands-on delivery. Located in a vibrant area, the company provides excellent benefits and fosters a strong sense of community, making it an ideal place for those seeking meaningful and rewarding employment.
StudySmarter Expert Advice🤫
We think this is how you could land Senior MLOps Engineer, Databricks
✨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 anything from model deployment to monitoring setups. Having tangible examples of your work can really set you apart during interviews.
✨Tip Number 3
Prepare for the technical grill! Brush up on your Python and PySpark skills, and be ready to discuss your experience with CI/CD and model governance. Practising common interview questions related to MLOps can help you feel more confident when it’s your turn to shine.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented individuals like you. Plus, applying directly can sometimes give you an edge over other candidates. So, what are you waiting for? Get that application in!
We think you need these skills to ace Senior MLOps Engineer, Databricks
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 and any relevant MLOps frameworks you've worked with. 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 demonstrate your ability to support data scientists in operationalising their models.
Be Clear and Concise:When writing your application, keep it clear and concise. Use bullet points where possible to make it easy for us to read through your experience and skills. We appreciate a straightforward approach!
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. We can’t wait to see what you bring to the table!
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 your knowledge of model deployment, monitoring, and CI/CD processes. Being able to discuss specific features and how they can be leveraged in a production environment will show that you're not just familiar with the platform, but that you can truly make it work for the team.
✨Showcase Your Hands-On Experience
Prepare to share concrete examples from your past roles where you've successfully managed MLOps environments or supported data scientists in deploying models. Highlight any frameworks or standards you've built that improved the path from development to production. This will demonstrate your practical experience and problem-solving skills.
✨Understand the Business Needs
Research the organisation and understand their goals regarding ML products. Be ready to discuss how you can align the Databricks roadmap with their business objectives. Showing that you can think strategically while being hands-on will set you apart as a candidate who can bridge the gap between technical and non-technical teams.
✨Prepare Questions for Them
Have insightful questions ready about their current MLOps setup and future plans. Ask about challenges they face in model governance or deployment. This not only shows your interest in the role but also your proactive approach to understanding how you can contribute to their success.
