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- Tasks: Design and deploy innovative machine learning solutions on the OVO AI Platform.
- Company: Join OVO, a leader in AI technology with a collaborative culture.
- Benefits: Competitive salary, flexible benefits, 34 days holiday, and health perks.
- Why this job: Make a real impact in AI while working with cutting-edge technologies.
- Qualifications: Strong Python skills and experience with ML frameworks and cloud platforms.
- Other info: Inclusive environment with excellent career growth opportunities.
The predicted salary is between 50000 - 72000 £ per year.
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Overview
Role: OVO – 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
Location: Hub offices in Bristol, Glasgow, or London (or field-based, as required). You’ll be assigned to the closest hub unless your role requires otherwise. Each hub provides spaces to park laptops and spaces to collaborate.
Role details
Team: ML Engineering
Team goal: Pioneer a sophisticated AI platform, centralising the operation of all OVO ML models to empower the company with superior AI solutions.
This role focuses on designing, building, and deploying machine learning solutions that drive business value on the OVO AI Platform, used by engineers and data scientists as a foundation to deliver AI models and use cases.
This is a great opportunity for someone motivated to advance ML practices across a broad business, in a collaborative and supportive environment.
Systems: Google Cloud Platform services (Vertex AI), GitHub
Responsibilities
- Help architect, design, and deploy new features and functionality on the OVO AI Platform to enable users to deliver high-quality ML and AI models at scale
- Collaborate with product managers, software developers, and business analysts to align ML initiatives with business goals
- Stay up-to-date with the latest ML trends, tools, and technologies to keep the team’s approach innovative
- Foster an inclusive team environment where everyone feels valued and can contribute
- Communicate effectively with collaborators to explain concepts and drive informed decision making
First 3 months
- Gain a comprehensive understanding of the AI platform and MLOps principles
- Collaborate on projects, providing insights on ML standard processes and software engineering guidelines
- Contribute to developing and enhancing our systems
- Educate other teams about effective ML practices and MLOps strategies
What makes you successful
- Excellent production-level Python programming, including testing (unit, integration, system) and knowledge of test-driven development; other languages are a plus
- Proficiency in ML frameworks (scikit-learn, XGBoost, TensorFlow, PyTorch)
- Proficiency with Cloud platforms (GCP, AWS, or Azure)
- Experience designing and deploying ML pipelines in production; Kubeflow Pipelines is a plus
- Solid understanding of ML principles, monitoring, security, and data preprocessing
- Familiarity with software engineering practices (design patterns, CI/CD, version control, containerisation, IaC/Terraform); Kubernetes knowledge a plus
- Strong communication skills to explain technical concepts to both technical and non-technical teammates
- Problem-solving mindset with ability to thrive in a collaborative, fast-paced environment
- Open-mindedness, cultural sensitivity, and commitment to an inclusive workplace
Compensation and benefits
Salary: £50,000 – £72,000, depending on skills and experience.
On-target bonus: 15%
Flexible benefits: 9% Flex Pay on top of salary, with 4% auto-enrolled into pension and 5% for flexible benefits, including green benefits, pension, or cash options.
Holidays: 34 days (including bank holidays).
Health and wellbeing: Healthcare cash plan or private medical insurance, critical illness cover, life assurance, health assessments, and more.
Wellbeing and lifestyle: Gym membership, travel insurance, workplace ISA, will writing services, dental insurance, and more.
Home and commute: OVO-affordable energy plans, discounts on solar, smart thermostats, EV chargers, cycle-to-work scheme, and public transport loans.
Belonging: 8 Belonging Networks to support an inclusive and diverse workplace.
Note: If you have additional requirements, there is a space to specify them on the application form.
Seniority level
- Mid-Senior level
Employment type
- Full-time
Job function
- Engineering and Information Technology
Industries
- Utilities and IT Services and IT Consulting
Referrals increase your chances of interviewing at OVO by 2x
London, England, United Kingdom
Recent related roles include AI Shopping Agents (Remote), Software Engineer (Python) – AI Platform, Data/ML Engineer roles in London.
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Machine Learning Engineer (AI Platform) employer: OVO
Contact Detail:
OVO Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer (AI Platform)
✨Tip Number 1
Network like a pro! Reach out to current employees at OVO on LinkedIn or through mutual connections. A friendly chat can give you insider info and might just get your foot in the door.
✨Tip Number 2
Show off your skills! Prepare a portfolio of your machine learning projects, especially those that align with OVO's goals. Being able to discuss your work in detail will impress interviewers.
✨Tip Number 3
Practice makes perfect! Get ready for technical interviews by brushing up on your Python and ML frameworks. Use platforms like LeetCode or HackerRank to sharpen your coding skills.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you’re genuinely interested in joining the OVO team.
We think you need these skills to ace Machine Learning Engineer (AI Platform)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Machine Learning Engineer role. Highlight your experience with Python, ML frameworks, and cloud platforms like GCP. We want to see how your skills align with what we're looking for!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about ML and how you can contribute to our AI platform. Keep it concise but impactful – we love a good story!
Show Off Your Projects: If you've worked on any relevant projects, make sure to mention them! Whether it's a personal project or something from a previous job, showcasing your hands-on experience can really set you apart.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us you're keen on joining our team at OVO!
How to prepare for a job interview at OVO
✨Know Your Tech Inside Out
Make sure you brush up on your Python programming skills, especially around testing and test-driven development. Familiarise yourself with ML frameworks like TensorFlow and PyTorch, as well as cloud platforms like GCP. Being able to discuss your technical expertise confidently will impress the interviewers.
✨Showcase Your Collaboration Skills
Since this role emphasises cross-collaboration, be prepared to share examples of how you've worked with product managers, software developers, or business analysts in the past. Highlight your adaptability and communication skills, as these are key qualities they’re looking for.
✨Stay Current with ML Trends
Keep yourself updated on the latest trends and tools in machine learning. Mention any recent projects or technologies you've explored that align with OVO's goals. This shows your commitment to innovation and your proactive approach to learning.
✨Prepare for Problem-Solving Scenarios
Expect to tackle some problem-solving questions during the interview. Think about challenges you've faced in previous roles and how you overcame them. Demonstrating a problem-solving mindset will resonate well with the interviewers, especially in a fast-paced environment.
