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: Competitive salary, excellent work-life balance, and potential for permanent role.
- Other info: Opportunity for career growth and leadership roles in a collaborative environment.
- Why this job: Shape the future of ML engineering and make a real impact.
- 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 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 machine learning within the organisation. The company is committed to investing in AI, providing you with the resources and support needed to make a significant impact in a dynamic environment.
StudySmarter Expert Advice🤫
We think this is how you could land Lead ML-Engineer
✨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. Practice makes perfect!
✨Tip Number 4
Don't forget to apply through our website! We’re always on the lookout for talented individuals like you. Plus, it’s a great way to ensure your application gets the attention it deserves.
We think you need these skills to ace Lead ML-Engineer
Some tips for your application 🫡
Tailor Your CV:Make sure your CV highlights your experience in ML engineering and MLOps. We want to see how you've taken projects from proof of concept to production, so don’t hold back on those details!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Tell us why you're passionate about AI and how you can contribute to our mission. Be specific about your skills and experiences that align with the role.
Showcase Your Technical Skills:We’re looking for hands-on experience, especially with cloud platforms like Azure and tools like Databricks. Make sure to mention any relevant projects or technologies you've worked with that demonstrate your expertise.
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!
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 MLOps practices and Azure. 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.
✨Demonstrate Your Problem-Solving Skills
Think of examples where you've tackled complex challenges in ML engineering. Whether it's optimising a model or managing data pipelines, be ready to explain your thought process and the impact of your solutions.
✨Ask Insightful Questions
Prepare some thoughtful questions about the company's AI strategy and how they envision the role evolving. This shows your genuine interest and helps you gauge if the company culture aligns with your values.
