Machine Learning Engineer (AI Platform)

Role OVO-View

Team: ML Engineering

Salary banding: £50,000 - £72,000

Experience: Mid-level

Working pattern: Full-Time

Reporting to: AI Platform Tech Lead

Sponsorship: Unfortunately we are unable to offer sponsorship for this role.

This role in 3 words: Cross-collaboration. Design. Ownership.

Top 3 qualities for this role: Adaptability, communication, technical skill

 

Where you’ll work:

Depending on the needs of your business area, we expect hub based people to be in the office at least once a week, and to go to OVO Connection events in-person. 

You’ll be assigned to the closest one of our three hub offices, Bristol, Glasgow, or London; unless your role requires field-based work. Each hub has accessible spaces to park your laptop, is designed to inspire people, help them connect and bring big ideas to life.

 

Everyone belongs at OVO

At OVO, we are on a mission to solve one of humanity's biggest challenges, the climate crisis. And we know it takes all of us to change the world. That's why we need diverse people from all abilities, gender identities, ethnicities, ages, sexual orientations, life experiences and backgrounds to join us.

 

Teamworking for the planet

Everything we do here spins around Plan Zero. So, naturally, the team you’ll be joining plays a gigantic role in making that happen. Here’s how:

Our team’s vision is to pioneer a sophisticated AI platform, centralising the operation of all OVOs ML models. Our goal is to empower our company with superior AI solutions. Whether we are solving climate change or another sophisticated challenge, we aim to make a difference. Be part of this progressive journey with us.

 

This role in a nutshell:

As a Machine Learning Engineer, you will work closely with fellow machine learning engineers to design, build, and deploy machine learning solutions that drive business value. To do this, you will develop new features, and functionalities on the OVO AI Platform, which is used by engineers, and data scientists as a foundation to deliver their AI models, and use cases.

This is a great opportunity for someone who is motivated to drive ML advancements, and best practices across a business with wide-ranging impact, and loves working in collaborative, and supportive environments.

 

Your key outcomes will be:

• Help architect, design, and deploy new features, and functionality on the OVO AI Platform to enable users to deliver high-quality machine learning, and artificial intelligence models at scale
• Team up with multi-functional teams, including product managers, software developers, and business analysts, to align machine learning initiatives with business goals.
• Stay up-to-date with the latest ML trends, tools, and technologies to ensure the team’s technical approach is innovative.
• Foster an inclusive team environment where everyone feels valued and excited to contribute.
• Communicate optimally with collaborators to explain concepts and drive informed decision making.

Within your first 3 months month you’ll:

• Gain a comprehensive understanding of our AI platform and associated MLOps principles.
• Begin collaborating on various projects, providing insights and advice on ML standard processes and software engineering guidelines.
• Contribute to the development and enhancing our systems
• Guide and educate other teams about effective ML practices and MLOps strategies.

Systems: Google Cloud Platform services (Vertex AI), GitHub

 

You’ll be a successful Machine Learning Engineer at OVO if you…

• Excellent production-level programming skills in Python; other languages are a plus.
• Proficiency in at least one ML framework, such as scikit-learn, XGBoost, Tensorflow, or PyTorch.
• Proficiency with Cloud platform(s), such as Google Cloud Platform, Amazon Web Services, or Azure.
• Experience in designing, and deploying ML pipelines in production environments; knowledge of Kubeflow Pipelines is a plus.
• Good understanding of ML principles, monitoring, security, and data preprocessing techniques.
• Familiarity with software engineering practices, such as software testing, design patterns, CI/CD, version control, containerisation, infrastructure as code/Terraform; knowledge of Kubernetes is a plus.