At a Glance
- Tasks: Lead the deployment of machine learning models and build scalable ML pipelines.
- Company: Join a major international organisation investing in data science and AI.
- Benefits: Competitive salary, flexible working, and opportunities for professional growth.
- Other info: Collaborative environment with excellent career advancement opportunities.
- Why this job: Make a real impact by bridging data science and engineering in exciting projects.
- Qualifications: Strong experience in Machine Learning Engineering and advanced Python skills.
The predicted salary is between 63000 - 77000 £ per year.
We're looking for a talented Lead Machine Learning Engineer to join a major international organisation investing heavily in its data science, machine learning and AI capabilities.
This is an exciting opportunity to take ownership of the engineering and deployment of machine learning solutions, moving models beyond experimentation and into scalable, production-ready environments.
You'll play a key role in bridging the gap between data science and engineering, developing robust ML pipelines, managing model lifecycles and ensuring machine learning solutions can be reliably deployed, monitored and scaled.
What you'll be working on
- Leading the deployment and operationalisation of machine learning models into production environments.
- Building and maintaining scalable ML pipelines using Python, SQL and Databricks.
- Implementing MLOps practices covering model lifecycle management, versioning, deployment and monitoring.
- Working with MLflow, model registries and production scoring frameworks.
- Developing and optimising Databricks workflows, notebooks and Delta Lake solutions.
- Establishing repeatable processes for model deployment, testing, monitoring and continuous improvement.
- Collaborating with Data Scientists, Data Engineers and wider technology teams to deliver production-grade ML solutions.
Experience
- Strong commercial experience in Machine Learning Engineering or MLOps.
- Advanced Python and SQL development skills.
- Proven experience deploying and operationalising machine learning models.
- Hands-on experience with Databricks, including notebooks, workflows and Delta Lake.
- Experience with MLflow, model registries and ML lifecycle management.
- Understanding of CI/CD, Git and Azure Dev Ops.
- Experience building scalable, reliable and maintainable machine learning solutions.
Additional experience that would be beneficial
- Databricks Asset Bundles and Databricks Apps.
- Model monitoring, performance tracking and automated retraining.
If you're a Machine Learning Engineer or MLOps specialist who enjoys taking models from development through to production, building robust ML infrastructure and delivering solutions at scale, we'd love to hear from you.
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Lead Machine Learning Engineer - Model Deployment & MLOps - New Team Build employer: GL Global
As a Legal Counsel / Corporate Solicitor at our prestigious FTSE 100 organisation, you will thrive in a fully remote environment that champions flexibility and work-life balance. Our inclusive work culture fosters collaboration and innovation, providing ample opportunities for professional growth and development while engaging with senior stakeholders on high-profile international transactions. Join us to be part of a dynamic team where your expertise in M&A will be valued and rewarded.
StudySmarter Expert Advice🤫
We think this is how you could land Lead Machine Learning Engineer - Model Deployment & MLOps - New Team Build
✨Join Local Tech Meetups
Get out there and mingle with fellow developers by joining local tech meetups. It’s a fantastic way to meet people who might be working at GL Global or know someone who does. Plus, you can pick up some trendy tech skills and trends while you're at it!
✨Contribute to Open Source Projects
Show off your coding chops by jumping into open-source projects. Not only does this give you practical experience, but it also gets you noticed in the dev community. You'll create a killer portfolio that speaks volumes about your skills to GL Global.
✨Tap into Online Developer Communities
Don’t underestimate the power of online developer communities like GitHub, Stack Overflow, and even Reddit. Participate in discussions, share your projects, and build your visibility. We can often find opportunities through these channels that can lead to a full-time gig at companies like GL Global.
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Keep your eyes peeled on job boards that focus on tech roles. Sites like TechCareers or Stack Overflow Jobs can often have listings for companies like GL Global that might not show up on broader job sites. Make it a habit to check these regularly, and don’t hesitate to apply directly through our website!
We think you need these skills to ace Lead Machine Learning Engineer - Model Deployment & MLOps - New Team Build
Some tips for your application 🫡
Show off your coding skills:When applying for a software engineering role, it's super important to showcase your coding skills. Make sure your CV includes your tech stack, any relevant programming languages you’re comfortable with, and examples of projects you've worked on. If you have a GitHub profile, link it up! We love to see code in action.
Tailor your portfolio:For a full-time role, we’d expect to see some solid examples of your work in your portfolio. Make sure to include at least two or three projects that highlight your problem-solving skills and your ability to work with different technologies. Focus on the projects that are most relevant to the position at GL Global.
Craft a killer cover letter:Your cover letter is your chance to stand out—make it personal! Explain why you want to work at GL Global and how your skills align with the role. Show us your passion for software development. We dig enthusiastic candidates who understand the value of collaboration and continuous learning!
Be clear and concise:When it comes to writing your CV and cover letter, clarity is key. Avoid jargon that could confuse us and stick to simple, direct language. Highlight your achievements with quantifiable results where possible, and keep everything easy to read. A well-organised application goes a long way!
How to prepare for a job interview at GL Global
✨Brush Up on Your Coding Skills
For a full-time software engineering role, it's crucial that we stay sharp with our coding abilities. Expect technical questions that might involve solving problems on the spot or discussing algorithms. Practise on platforms like LeetCode or HackerRank to get comfortable with the types of questions that often come up.
✨Know Your Tools and Frameworks
Make sure we’re well-acquainted with the tools and technologies listed in the job description. Familiarise ourselves with any specific frameworks or programming languages mentioned. If GL Global uses React or Node.js, for instance, be ready to discuss how we’ve used them in previous projects or coursework.
✨Showcase Your Projects
Bring along a portfolio that highlights our best work. This could be code samples, GitHub repositories, or any side projects we’ve built. Make sure we can talk through our thought process for each project, especially the challenges we faced and how we solved them—this shows our problem-solving skills in action.
✨Prepare for Behavioural Questions
While technical skills are key, full-time positions also require cultural fit. Be ready to discuss our previous experiences and how we handle teamwork, conflict, and deadlines. Brush up on the STAR method—Situation, Task, Action, Result—to clearly articulate our past experiences when discussing how we've contributed to a team.
