At a Glance
- Tasks: Lead the engineering and deployment of cutting-edge ML and AI solutions.
- Company: Dynamic company investing heavily in AI with a supportive culture.
- Benefits: Up to £90,000 salary, excellent work-life balance, and potential for permanent role.
- Other info: Collaborative environment with opportunities for leadership and career growth.
- Why this job: Shape the future of ML engineering and make a real impact in AI.
- Qualifications: Strong ML engineering experience, Python skills, and cloud platform knowledge.
The predicted salary is between 90000 - 90000 € per year.
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.
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.
- 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.
- 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.
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. 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.
Lead ML Engineer (Permanent) employer: Harnham
As a Lead Machine Learning Engineer, you will join a forward-thinking company that prioritises innovation and employee well-being. With a strong emphasis on collaboration and professional growth, this remote role offers an excellent work-life balance and the opportunity to shape the future of AI solutions within the organisation. The supportive culture and commitment to long-term outcomes make it an ideal environment for those seeking meaningful and rewarding employment in the tech industry.
StudySmarter Expert Advice🤫
We think this is how you could land Lead ML Engineer (Permanent)
✨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 seen. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Lead ML Engineer (Permanent)
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that align with the Lead ML Engineer role. Highlight your hands-on experience with cloud platforms like Azure and your knowledge of MLOps practices. We want to see how you can contribute to our mission!
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 makes you a perfect fit for this role. Don’t forget to mention any relevant projects or achievements that showcase your expertise.
Showcase Your Projects:If you've worked on any interesting ML projects, make sure to include them in your application. Whether it's building end-to-end ML pipelines or deploying models, we love seeing real-world applications of your skills. It helps us understand your practical experience better!
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 the role. Plus, it gives you a chance to explore more about what we do at StudySmarter!
How to prepare for a job interview at Harnham
✨Know Your ML Stuff
Make sure you brush up on your machine learning concepts and practices. Be ready to discuss your experience with deploying and maintaining ML systems in production, especially on cloud platforms like Azure. They’ll want to see that you can talk the talk and walk the walk!
✨Showcase Your Projects
Prepare to share specific examples of projects you've worked on, particularly those involving end-to-end ML pipelines or generative AI solutions. Highlight your role in turning POCs into scalable services and how you’ve collaborated with data scientists and engineers.
✨Demonstrate MLOps Knowledge
Since MLOps is a key part of this role, be ready to discuss your experience with CI/CD, model serving, and observability. Bring examples of how you've implemented these practices in past roles to show you understand the full delivery lifecycle.
✨Ask Insightful Questions
Prepare thoughtful questions about their current ML initiatives and future plans. This shows your genuine interest in the role and helps you gauge if the company’s culture and goals align with your own aspirations in ML engineering.
