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- Tasks: Build and scale ML systems for clean energy solutions.
- Company: Fast-growing London-based energy tech company focused on sustainable transport.
- Benefits: Competitive salary, hybrid working, and opportunities for professional growth.
- Why this job: Join a mission-driven team making a real impact in the clean energy transition.
- Qualifications: Experience with ML models, AWS, and strong problem-solving skills.
- Other info: Dynamic environment with a focus on innovation and sustainability.
The predicted salary is between 80000 - 110000 £ per year.
Do you want to build production-grade ML systems powering the clean energy transition? Have you deployed and scaled ML models on AWS in real-world environments? Are you ready to take ownership of MLOps in a fast-growing cleantech scale-up?
A rapidly scaling London-based energy tech company is building an intelligent EV charging and energy optimisation platform used across multiple countries. They’ve combined proprietary tech, deep data capability and strong industry partnerships to accelerate the shift to sustainable transport. The Data team is now expanding to support growing ML workloads.
They’re hiring an MLOps Engineer to own and scale ML infrastructure across computer vision and broader DS/ML use cases. This role is critical to ensuring models are robust, reproducible and production-ready.
Key responsibilities:- Deploy, manage and monitor ML models in production
- Own MLflow, experimentation tracking and reproducibility
- Build scalable training and deployment pipelines
- Implement CI/CD for ML workflows
- Optimise AWS infrastructure for performance and cost
- Ensure reliability and monitoring of ML endpoints
- Salary: £80,000–£110,000 + discretionary bonus
- Working: Hybrid, 3 days per week in London
- Stack: Python, MLflow, AWS (SageMaker), CDK, Docker
- Visa: Cannot sponsor
Interested? Please apply below.
MLOps Engineer - Energy AI Platform in London employer: Harnham
Contact Detail:
Harnham Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land MLOps Engineer - Energy AI Platform in London
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups or webinars, and connect with current employees at the company. You never know who might give you a heads-up about job openings or even refer you directly!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your MLOps projects, especially those involving AWS and MLflow. This will not only demonstrate your expertise but also give you something tangible to discuss during interviews.
✨Tip Number 3
Prepare for technical interviews by brushing up on key concepts related to ML deployment and monitoring. Practice coding challenges and be ready to explain your thought process clearly. We want you 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 are proactive and engaged with our platform.
We think you need these skills to ace MLOps Engineer - Energy AI Platform in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with deploying and scaling ML models, especially on AWS. We want to see how your skills align with our mission in the clean energy sector!
Showcase Your Projects: Include specific examples of projects where you've built scalable training and deployment pipelines. We love seeing real-world applications of your work, so don’t hold back!
Be Clear and Concise: When writing your cover letter, keep it straightforward. We appreciate clarity, so get to the point about why you’re a great fit for the MLOps Engineer role and how you can contribute to our team.
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 in our growing team!
How to prepare for a job interview at Harnham
✨Know Your Tech Stack
Make sure you’re well-versed in the technologies mentioned in the job description, like Python, MLflow, and AWS. Brush up on your experience with deploying ML models and managing infrastructure, as these will be key discussion points.
✨Showcase Real-World Experience
Prepare to discuss specific projects where you've deployed and scaled ML models in production. Highlight any challenges you faced and how you overcame them, especially in relation to clean energy or similar sectors.
✨Understand MLOps Principles
Familiarise yourself with MLOps best practices, including CI/CD for ML workflows and experimentation tracking. Be ready to explain how you would implement these in a fast-paced environment, demonstrating your ownership mindset.
✨Ask Insightful Questions
Prepare thoughtful questions about the company’s approach to ML infrastructure and their vision for the energy tech space. This shows your genuine interest and helps you assess if the role aligns with your career goals.
