NO AGENCIES PLEASE

Must be eligible to work in the UK and have resident status.

Role is based in Reading, UK.

Applications closing date: 20^th Feb 2025

At measurable.energy, we are dedicated to advancing the global decarbonisation agenda by revolutionising the building industry. Our innovative smart plug socket technology hunts for and eliminates wasted energy and emissions, addressing up to 40% of energy inefficiencies in commercial buildings. We combine patented hardware and software solutions powered by Machine Learning to optimise energy consumption automatically. Recognised as one of the fastest-growing CleanTech scale-ups in the UK, we have been featured on prominent platforms such as the BBC, presented at COP26, and acclaimed by Tech Nation as a leading climate tech company.

About the role:

We are looking for an ML-Ops Engineer with expertise in managing, deploying, and optimising machine learning models, particularly in the domain of electrical data. In this role, the ideal candidate will bridge the gap between data science and engineering, ensuring robust pipelines for model development, deployment, and monitoring.

The ideal candidate is passionate about learning new skills and is excited by the opportunity to work with a completely unique dataset at a fast-growing scale-up.

The role is based in our Reading office, with the ability to work remotely up to 2 days per week.

Key responsibilities:

ML Pipeline Development and Optimisation:

• Design, build, and maintain scalable ML pipelines for training, validation, and deployment of models.
• Optimise pipelines to handle electrical data, ensuring accuracy, reliability, and efficiency.

Deployment and Monitoring:

• Deploy machine learning models to production environments using modern MLOps frameworks and tools.
• Implement alerting systems for inference execution.

Data Management:

• Develop strategies for handling time-series data, anomalies, and missing data points common in electrical systems.
• Ensure adherence to data governance and security standards.

Collaboration with Teams:

• Work closely with software engineers and domain experts to translate business requirements into deployable ML solutions.

Tooling and Infrastructure:

• Implement and manage cloud-based platforms in AWS for model training and deployment.
• Leverage containerisation tools (e.g., Docker, Kubernetes) for scalable model deployments.

Essential skills:

• 1-3 years’ experience in ML-Ops, software engineering, or data engineering roles.
• Strong mathematical ability.
• Strong programming skills in Python, with familiarity in ML libraries such as TensorFlow, PyTorch, or Scikit-learn.
• Ability to query data with SQL.
• Proficiency in cloud platforms like AWS, Azure, or GCP.
• Experience with CI/CD pipelines and containerisation technologies (e.g., Docker, Kubernetes).
• Strong understanding of data engineering concepts, including ETL pipelines and data lakes.

Desirable skills:

• Familiarity with electrical systems, smart grid data, or industrial IoT.
• Hands-on experience with ML-Ops tools and frameworks such as SageMaker.
• Expertise in database systems for time-series data.
• Advanced knowledge of monitoring and observability tools for ML models.

Package:

Salary: 30,000 – 40,000 GBP annually

Holiday: 25 days plus bank holidays

Benefits:

- Private Health Cover

- Hybrid working

- Flexible working

- Career development pathway and progression

- Exceptional team working environment

Join us in our mission to revolutionise energy efficiency and contribute to a sustainable future. If you are passionate about making a positive impact and possess the skills and drive to excel in this role, we want to hear from you. Apply now to be part of our innovative team at measurable.energy. Please apply by sending an up-to-date CV to nathan@measurable.energy.