At a Glance
- Tasks: Lead the engineering of machine learning solutions and build end-to-end ML pipelines.
- Company: Established UK organisation prioritising AI and data-driven innovation.
- Benefits: Competitive salary, excellent work-life balance, and potential for permanent role.
- Other info: Collaborative culture with opportunities for future leadership roles.
- Why this job: Shape ML engineering standards and make a real impact in a growing field.
- Qualifications: Strong ML engineering experience, cloud platform knowledge, and Python skills.
The predicted salary is between 90000 - 90000 € per year.
12 Month FTC Remote UK, up to £90,000, 12 month fixed term contract. This is a rare opportunity to take ownership of machine learning delivery in a business that is actively investing in AI and moving from proof of concept into production. You will play a pivotal role in shaping how advanced ML and generative AI solutions are engineered, deployed and scaled, with genuine scope for the role to become permanent.
The Company
They are a large, well established UK organisation operating in a highly regulated, information rich environment. With thousands of colleagues nationwide, they combine deep domain expertise with a strong focus on people, quality and long term outcomes. Data and AI are now a strategic priority, with senior backing to build robust, production grade ML capability.
The Role
- Lead the engineering and productionisation of machine learning and generative AI solutions.
- Build and operate end to end ML pipelines, including data preparation, model deployment, monitoring and governance.
- Work closely with data scientists and data engineers to turn experiments and POCs into scalable, reliable services.
- Develop solutions for large scale unstructured data, including complex document processing and LLM ready data pipelines.
- Own MLOps practices, covering CI/CD, model serving, observability and lifecycle management.
- Provide hands on technical leadership, contributing to architecture decisions and best practice.
- Act as a delivery focused partner to stakeholders, confidently explaining trade offs and recommendations.
Your Skills & Experience
- Strong commercial experience as an ML Engineer or MLOps focused engineer, ideally with a software engineering background.
- Proven ability to deploy, operate and maintain machine learning systems in production.
- Hands on experience with cloud based data and ML platforms, particularly on Azure.
- Solid knowledge of Databricks and modern data engineering concepts such as lakehouse architectures.
- Experience preparing data and pipelines for LLM based use cases and NLP workloads.
- Strong Python skills, with experience building APIs or services, for example using FastAPI.
- Confidence working across the full delivery lifecycle, from design through to monitoring and optimisation.
- Clear communication skills and comfort working directly with non technical stakeholders.
What They Offer
- Excellent work life balance and a supportive, collaborative culture.
- The chance to shape ML engineering standards and capability from the ground up.
- Strong potential for the role to become permanent, with future people leadership opportunities.
How to Apply
If you are an experienced ML Engineer looking for a hands on role with real influence and long term potential, apply now to find out more.
(198488) Lead ML Engineer in Liverpool employer: LinkedIn
As a Lead Machine Learning Engineer at this large, well-established UK organisation, you will thrive in a supportive and collaborative culture that prioritises work-life balance while actively investing in AI. With the opportunity to shape ML engineering standards and capabilities from the ground up, you will benefit from strong potential for permanent placement and future leadership roles, making it an excellent employer for those seeking meaningful and rewarding employment.
StudySmarter Expert Advice🤫
We think this is how you could land (198488) Lead ML Engineer in Liverpool
✨Tip Number 1
Network like a pro! Reach out to your connections in the ML and AI space. Attend meetups, webinars, or even online forums. 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 ML projects, especially those involving cloud platforms like Azure or Databricks. This will give potential employers a taste of what you can bring to the table.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and soft skills. Be ready to discuss your experience with MLOps practices and how you've tackled challenges in previous roles. Confidence is key!
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace (198488) Lead ML Engineer in Liverpool
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences mentioned in the job description. Highlight your hands-on experience with ML systems, cloud platforms like Azure, and any relevant projects you've worked on. We want to see how you can bring value to our team!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about machine learning and how your background aligns with our needs. Be sure to mention your experience with MLOps practices and your ability to communicate with non-technical stakeholders.
Showcase Your Projects:If you've worked on any interesting ML projects, make sure to include them in your application. Whether it's building APIs with FastAPI or developing data pipelines for LLM use cases, we love seeing real-world applications of your skills!
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for this exciting opportunity. Don’t miss out on the chance to join our team!
How to prepare for a job interview at LinkedIn
✨Know Your ML Stuff
Make sure you brush up on your machine learning concepts and tools, especially those mentioned in the job description like Azure and Databricks. Be ready to discuss your hands-on experience with deploying and maintaining ML systems in production.
✨Showcase Your Leadership Skills
Since this role involves technical leadership, prepare examples of how you've led projects or teams in the past. Think about times when you made architecture decisions or improved MLOps practices, and be ready to share these stories.
✨Communicate Clearly
You’ll need to explain complex ideas to non-technical stakeholders, so practice simplifying your explanations. Use clear, jargon-free language to describe your past projects and the trade-offs you’ve navigated.
✨Prepare for Scenario Questions
Expect questions that ask how you would handle specific challenges, like scaling ML solutions or managing data pipelines. Think through potential scenarios and how you would approach them, demonstrating your problem-solving skills and strategic thinking.
