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For Companies At a Glance
- Tasks: Lead ML projects, engage in pre-sales, and deploy models to production.
- Company: Join a cutting-edge AI consultancy revolutionising the industry.
- Benefits: Enjoy remote work flexibility and a competitive salary up to £110,000.
- Why this job: Be part of an innovative team making a real impact in AI technology.
- Qualifications: 8+ years in Data Science/ML Engineering and 2+ years with Databricks required.
- Other info: Ideal for those with a PhD/postdoc in AI/ML and strong MLOps knowledge.
The predicted salary is between 66000 - 88000 £ per year.
Job Description
🚨 Hiring: Snr. Databricks ML Engineer
📍 Remote (UK-based) | 💰 Up to £110,000
We’re working with a cutting-edge AI consultancy who are scaling their ML team with a key hire: a Senior Databricks ML Engineer who’s as comfortable in pre-sales conversations as they are deploying models to production.
🧠 You’ll be someone who:
- Has deep ML theory knowledge — ideally a PhD/postdoc in AI/ML
- Knows when not to use a model based on cost vs. impact
- Advocates for AI productivity tools and uses them daily
- Is commercially minded, tech-savvy, and client-facing
🎯 Must-haves:
- 8+ years in Data Science / ML Engineering
- 2+ years hands-on with Databricks
- Strong track record delivering production-grade ML models
- Solid grasp of MLOps best practices
- Confident speaking to technical and non-technical stakeholders
🛠️ Tech you’ll be using:
- Python, SQL, Spark, R
- MLflow, vector databases
- GitHub / GitLab / Azure DevOps
- Jira, Confluence
🎓 Bonus points for:
- MSc/PhD in ML or AI
- Databricks ML Engineer (Professional) certified
Senior Machine Learning Engineer employer: Omnis Partners
Contact Detail:
Omnis Partners Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Machine Learning Engineer
✨Tip Number 1
Familiarise yourself with Databricks and its ecosystem. Since the role requires hands-on experience with Databricks, consider exploring their documentation and tutorials to understand best practices and features that can enhance your ML projects.
✨Tip Number 2
Engage in relevant online communities or forums focused on machine learning and Databricks. Networking with professionals in these spaces can provide insights into industry trends and may even lead to referrals for the position.
✨Tip Number 3
Prepare to discuss your previous projects in detail, especially those involving MLOps and production-grade models. Being able to articulate your thought process and decision-making will demonstrate your expertise and confidence to potential employers.
✨Tip Number 4
Brush up on your communication skills, particularly in explaining complex technical concepts to non-technical stakeholders. This is crucial for a client-facing role, so practice articulating your ideas clearly and concisely.
We think you need these skills to ace Senior Machine Learning Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in Data Science and ML Engineering, especially your 8+ years of relevant work. Emphasise your hands-on experience with Databricks and any production-grade ML models you've delivered.
Craft a Compelling Cover Letter: In your cover letter, showcase your deep knowledge of ML theory and your ability to communicate with both technical and non-technical stakeholders. Mention specific projects where you’ve successfully deployed models and how you advocate for AI productivity tools.
Highlight Relevant Skills: Clearly list your technical skills such as Python, SQL, Spark, and MLOps best practices. If you have certifications like the Databricks ML Engineer (Professional), make sure to include them as they can set you apart from other candidates.
Showcase Your Commercial Mindset: Demonstrate your understanding of the balance between cost and impact when using models. Provide examples of how you've made decisions based on this principle in your previous roles, as this is crucial for the position.
How to prepare for a job interview at Omnis Partners
✨Showcase Your Technical Expertise
Be prepared to discuss your deep knowledge of machine learning theory and practical applications. Highlight specific projects where you've successfully deployed models, especially using Databricks, and be ready to explain your decision-making process regarding model selection.
✨Demonstrate Commercial Awareness
Since the role requires a commercially minded approach, think about how your technical skills can translate into business value. Prepare examples of how you've balanced cost versus impact in previous projects, and be ready to discuss how you can contribute to the company's bottom line.
✨Engage with Stakeholders
Practice explaining complex technical concepts in simple terms, as you'll need to communicate effectively with both technical and non-technical stakeholders. Consider preparing a few scenarios where you've successfully navigated these conversations in the past.
✨Familiarise Yourself with MLOps Best Practices
Since a solid grasp of MLOps is essential, review best practices and be ready to discuss how you've implemented them in your work. Think about how you can advocate for AI productivity tools and share your experiences using them in daily operations.
