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- Tasks: Design and optimise ML systems for cleaner energy solutions.
- Company: Fast-growing cleantech scale-up with a collaborative culture.
- Benefits: Competitive salary, hybrid work, and rapid development opportunities.
- Why this job: Join a mission-driven team and make a real impact on clean energy.
- Qualifications: Experience in Python, MLOps, AWS, and building scalable ML pipelines.
- Other info: High ownership role with exposure to modern ML tooling.
The predicted salary is between 72000 - 84000 £ per year.
This is an exciting opportunity to join a mission driven cleantech scale up as they continue to grow their data and AI function. You will help shape a modern MLOps environment, working on impactful machine learning deployments that directly support smarter, cleaner energy systems.
The Company: They are a fast growing technology scale up within the energy and electric space. The team is collaborative, customer focused, and driven by a strong product mindset. You would be joining a small, high impact data group with experienced engineers and the opportunity to take real ownership.
The Role: In this MLOps Engineer role, you will contribute to the design, deployment and optimisation of production ML systems. Responsibilities include:
- Supporting data scientists and AI engineers to build, deploy and monitor ML models in production environments.
- Managing ML lifecycle.
- Designing scalable ML pipelines for training, validation and deployment.
- Implementing CI/CD workflows for machine learning and maintaining reliable ML endpoints.
- Working heavily with AWS, including SageMaker, to deliver robust, secure and scalable ML infrastructure.
- Applying strong engineering standards across cloud, DevOps and automation practices.
- Contributing to computer vision and broader ML workloads, with scope to support new AI initiatives as they grow.
Your Skills and Experience: To succeed, you will bring strong commercial experience in:
- Python and applied ML engineering.
- MLOps tooling such as MLflow and modern experiment tracking platforms.
- Deploying models into production, including monitoring, testing and automation.
- AWS, with practical experience using SageMaker.
- Cloud and DevOps foundations including Docker and AWS.
- Building scalable data and ML pipelines with solid engineering practices.
You work well in fast paced environments, communicate clearly, and enjoy collaborating with cross functional teams.
What They Offer: Competitive salary plus discretionary bonus. Hybrid working with three days each week in their London office. A high impact role within a growing data and AI team. Strong ownership, rapid development opportunities and exposure to modern ML tooling. A mission led environment focused on accelerating the transition to clean energy.
How To Apply: Please register your interest by sending your CV to Madison Barlow via the Apply link on this page.
MLOps Engineer employer: Harnham - Data & Analytics Recruitment
Contact Detail:
Harnham - Data & Analytics Recruitment Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land MLOps Engineer
✨Tip Number 1
Network like a pro! Reach out to people in the industry, especially those working in MLOps or at companies you're interested in. A friendly chat can lead to insider info and even referrals.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your MLOps projects, especially any that involve AWS or ML pipelines. This gives you a chance to demonstrate your expertise beyond just your CV.
✨Tip Number 3
Prepare for interviews by brushing up on common MLOps scenarios. Think about how you'd handle deploying models or managing ML lifecycles. Practising these will help you feel more confident when it’s your turn to shine.
✨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 take the initiative to connect directly with us.
We think you need these skills to ace MLOps Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights the skills and experiences that match the MLOps Engineer role. Use keywords from the job description to show we’re on the same page!
Showcase Your Projects: Include any relevant projects or experiences where you've deployed ML models or worked with AWS. We love seeing real-world applications of your skills!
Keep It Clear and Concise: Your application should be easy to read. Avoid jargon and keep your sentences straightforward. We appreciate clarity just as much as you do!
Apply Through Our Website: Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you don’t miss out on this exciting opportunity.
How to prepare for a job interview at Harnham - Data & Analytics Recruitment
✨Know Your MLOps Inside Out
Make sure you brush up on your MLOps knowledge before the interview. Be ready to discuss your experience with ML lifecycle management, CI/CD workflows, and tools like MLflow. The more specific examples you can provide about your past projects, the better!
✨Showcase Your AWS Skills
Since this role heavily involves AWS, particularly SageMaker, be prepared to talk about your hands-on experience with these technologies. Share any challenges you've faced and how you overcame them, as well as any best practices you've implemented in your previous roles.
✨Demonstrate Collaboration and Communication
This company values teamwork, so highlight your ability to work with cross-functional teams. Prepare examples of how you've successfully collaborated with data scientists or AI engineers in the past, and how you communicated complex technical concepts to non-technical stakeholders.
✨Prepare Questions That Matter
At the end of the interview, you'll likely have a chance to ask questions. Use this opportunity to show your interest in their mission and culture. Ask about their current projects in clean energy or how they envision the future of their MLOps environment. This shows you're not just interested in the job, but also in contributing to their goals.