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- Tasks: Lead the design and deployment of machine learning models on a cutting-edge Lakehouse platform.
- Company: Join a forward-thinking company revolutionising enterprise machine learning.
- Benefits: Enjoy hybrid working, bonuses, private medical cover, and a supportive environment.
- Why this job: Shape the future of ML with innovative technologies and make impactful decisions.
- Qualifications: Degree in STEM, strong Python and SQL skills, and MLOps experience required.
- Other info: Collaborative culture with opportunities for professional growth and knowledge sharing.
The predicted salary is between 36000 - 60000 £ per year.
This is an opportunity to shape how enterprise machine learning is delivered at scale within a modern data environment. Working on a strategic Lakehouse platform built on Databricks, you will influence how production ML models are designed, governed and optimised, helping the business turn complex data into reliable, decision-driving insight. The role combines hands-on engineering with MLOps leadership, strong stakeholder engagement and long-term platform thinking.
Responsibilities
- Lead the design, build, deployment and monitoring of production machine learning models using Databricks, ensuring performance, reliability and continuous improvement.
- Define and embed MLOps best practices including model versioning, governance, access control, monitoring and retraining strategies.
- Develop automated model validation tests covering unit, integration, regression and bias checks.
- Translate business problems into effective ML solutions, managing ethical, privacy and data governance considerations.
- Establish model performance KPIs, reliability measures and production monitoring frameworks.
- Document model design, assumptions, metrics, risks and failure scenarios, ensuring full data and model traceability.
- Diagnose and resolve production ML issues, leading root cause analysis and system improvements.
- Work within cross-functional agile teams and support knowledge sharing across the wider business.
Requirements
- Degree in a STEM subject or equivalent experience with strong statistical understanding.
- Proven experience delivering machine learning models into production environments.
- Strong Python, SQL and PySpark skills for scalable, production-grade development.
- Hands-on experience establishing MLOps processes within Databricks.
- Experience building data pipelines for structured and unstructured data.
- Strong stakeholder communication skills, able to explain technical concepts to non-technical audiences.
- Experience working in agile delivery environments and managing shifting priorities.
What's in it for me?
- Hybrid working model.
- Discretionary bonus.
- Non-contributory pension.
- Private medical and dental cover (including family options).
- Life insurance and wellbeing-focused benefits.
- Supportive, collaborative data and technology environment.
- Opportunity to work on modern ML, MLOps and Lakehouse technologies at scale.
Senior Machine Learning Engineer in Manchester employer: Fruition Group
Contact Detail:
Fruition Group Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Machine Learning Engineer in Manchester
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups or webinars, and connect with people on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects, especially those involving Databricks. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on MLOps best practices and be ready to discuss how you've tackled real-world problems. Practice explaining complex concepts in simple terms – it’s key to impressing non-technical stakeholders!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace Senior Machine Learning Engineer in Manchester
Some tips for your application 🫡
Tailor Your CV: Make sure your CV speaks directly to the role of Senior Machine Learning Engineer. Highlight your experience with Databricks, MLOps, and any relevant projects that showcase your skills in Python, SQL, and PySpark.
Craft a Compelling Cover Letter: Use your cover letter to tell us why you're passionate about machine learning and how you can contribute to our Lakehouse platform. Share specific examples of how you've tackled similar challenges in the past.
Showcase Your Problem-Solving Skills: In your application, don’t just list your skills—demonstrate them! Describe how you've translated complex business problems into effective ML solutions, and how you’ve managed ethical and governance considerations.
Apply Through Our Website: We encourage you to apply through our website for a smoother process. It helps us keep track of your application and ensures you get all the updates directly from us!
How to prepare for a job interview at Fruition Group
✨Know Your Tech Inside Out
Make sure you’re well-versed in the technologies mentioned in the job description, especially Databricks, Python, SQL, and PySpark. Brush up on your MLOps best practices and be ready to discuss how you've implemented them in past projects.
✨Prepare Real-World Examples
Think of specific instances where you've designed, built, or deployed machine learning models. Be ready to explain the challenges you faced, how you overcame them, and the impact your work had on the business. This will show your hands-on experience and problem-solving skills.
✨Understand the Business Context
Familiarise yourself with how machine learning can drive insights in a business setting. Be prepared to discuss how you would translate complex business problems into effective ML solutions, considering ethical and governance aspects.
✨Engage with Stakeholders
Since strong stakeholder communication is key, practice explaining technical concepts in simple terms. Think about how you would engage with non-technical audiences and ensure they understand the value of your work. This will demonstrate your ability to bridge the gap between tech and business.
