At a Glance
- Tasks: Lead the engineering and deployment of cutting-edge ML and AI solutions.
- Company: Established UK organisation prioritising data and AI with a collaborative culture.
- Benefits: Competitive salary, excellent work-life balance, and potential for permanent role.
- Other info: Opportunity for career growth and leadership roles in a dynamic environment.
- Why this job: Shape ML engineering standards and make a real impact in AI development.
- Qualifications: Strong ML engineering experience, cloud platforms knowledge, and Python skills.
The predicted salary is between 90000 - 90000 € per year.
Lead Machine Learning Engineer 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 England employer: Harnham
As a leading UK organisation, this company offers an exceptional work environment that prioritises employee well-being and professional growth. With a strong focus on collaboration and innovation in the field of machine learning, employees are empowered to shape engineering standards while enjoying a healthy work-life balance. The potential for the role to transition into a permanent position, along with future leadership opportunities, makes this an attractive place for those seeking meaningful and rewarding employment.
StudySmarter Expert Advice🤫
We think this is how you could land (198488) Lead ML Engineer in England
✨Tip Number 1
Network like a pro! Reach out to your connections in the industry, attend meetups, and engage in online forums. The more people you know, the better your chances of hearing about opportunities before they even hit the job boards.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects, especially those that highlight your experience with cloud platforms like Azure and 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! We love seeing candidates who are genuinely interested in joining us. Tailor your application to reflect your passion for ML engineering and how you can contribute to our mission.
We think you need these skills to ace (198488) Lead ML Engineer in England
Some tips for your application 🫡
Tailor Your CV:Make sure your CV highlights your experience in ML engineering and MLOps. Use keywords from the job description to show we’re on the same page about what you bring to the table.
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Share specific examples of your past projects, especially those involving cloud platforms like Azure or Databricks, to demonstrate your hands-on experience.
Showcase Your Technical Skills:Don’t hold back on your technical skills! Mention your proficiency in Python and any experience with APIs or services. We want to see how you can contribute to our ML pipelines and governance.
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 don’t miss out on any important updates!
How to prepare for a job interview at Harnham
✨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, Databricks, and MLOps practices. Be ready to discuss your hands-on experience with deploying and maintaining ML systems in production.
✨Showcase Your Projects
Prepare to talk about specific projects where you've built end-to-end ML pipelines or worked with unstructured data. Highlight your role in turning POCs into scalable services and how you collaborated with data scientists and engineers.
✨Communicate Clearly
Since you'll be working with non-technical stakeholders, practice explaining complex technical concepts in simple terms. This will show that you can bridge the gap between tech and business, which is crucial for this role.
✨Ask Insightful Questions
Prepare thoughtful questions about the company's AI strategy and how they envision the ML engineering role evolving. This not only shows your interest but also helps you gauge if the company aligns with your career goals.
