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- Tasks: Lead a team to deploy and scale machine learning models in production.
- Company: Join a forward-thinking company building a new ML Engineering team.
- Benefits: Competitive salary, flexible hybrid work, and opportunities for professional growth.
- Other info: Dynamic environment with a focus on collaboration and innovation.
- Why this job: Make a real impact by shaping the future of machine learning solutions.
- Qualifications: 5+ years as an ML Engineer with strong Python skills and cloud experience.
The predicted salary is between 60000 - 80000 £ per year.
We're building a new ML Engineering team and are looking for a strong technical lead to help take our machine learning capability from proof-of-concept to fully scaled, production-ready solutions. Sitting within our Group & Enterprise Services (GES) function, this role is part of the Data vertical and reports into the Head of Data Engineering. You'll be hands-on with cloud infrastructure, APIs and deployment pipelines, working mainly in GCP Vertex AI (essential) and Azure (desirable). Your focus will be enabling data scientists to deploy high-impact models reliably and at scale. You'll combine leadership, architectural thinking and deep engineering skills to shape the ML platform, coach engineers and deliver robust, enterprise-ready ML services.
What you'll do:
- Lead, mentor and develop a small team of ML Engineers
- Oversee delivery of ML capabilities and support planning and capacity needs
- Shape architecture from early design through to production
- Build and maintain Python APIs (Flask/FastAPI) for model serving
- Develop infrastructure for real-time and batch deployments
- Design and maintain CI/CD pipelines for models
- Ensure code quality, engineering best practice and scalable cloud deployments
- Collaborate with data scientists, platform engineers and developers
- Support model lifecycle management, monitoring and automation
- Break down solution designs into deliverables and milestones
What you'll bring:
- 5+ years as an ML Engineer with strong Python engineering skills
- Experience deploying and maintaining ML models in production (Vertex AI required)
- Strong software engineering fundamentals: OOP, unit testing, TDD
- Cloud experience (GCP, AWS or Azure) and IaC tools such as Terraform
- Experience with Docker, CI/CD pipelines and Git workflows
- Understanding of data science principles and taking research code to production
- Strong problem-solving skills and the ability to work independently
- Comfortable working in Agile teams
- Clear communication, collaboration and a proactive, improvement-driven mindset
ML Ops Engineer | York Hybrid employer: Oliver James
Contact Detail:
Oliver James Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land ML Ops Engineer | York Hybrid
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups or webinars, and connect with people on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your ML projects, especially those involving GCP Vertex AI. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and soft skills. Practice explaining complex concepts simply, as you'll need to communicate effectively with both technical and non-technical team members.
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are genuinely interested in joining our team.
We think you need these skills to ace ML Ops Engineer | York Hybrid
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the ML Ops Engineer role. Highlight your experience with GCP Vertex AI and any relevant projects you've worked on. We want to see how your skills align with what we're looking for!
Showcase Your Projects: Include specific examples of ML models you've deployed in production. Talk about the challenges you faced and how you overcame them. This will help us understand your problem-solving skills and hands-on experience.
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Explain why you're excited about joining our team and how you can contribute to our ML capabilities. Keep it concise but impactful – we love a good story!
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’re considered for the role. Plus, it makes the process smoother for everyone involved.
How to prepare for a job interview at Oliver James
✨Know Your Tech Inside Out
Make sure you’re well-versed in GCP Vertex AI and Azure, as these are essential for the role. Brush up on your Python skills, especially around building APIs with Flask or FastAPI, and be ready to discuss your experience with CI/CD pipelines and Docker.
✨Showcase Your Leadership Skills
Since this role involves mentoring a small team, be prepared to share examples of how you've led projects or coached others in the past. Highlight your ability to shape architecture and deliver robust ML services, as this will demonstrate your readiness for a leadership position.
✨Prepare for Problem-Solving Questions
Expect to face questions that test your problem-solving abilities. Think of specific challenges you've encountered in deploying ML models and how you overcame them. This will show your analytical skills and your capacity to work independently.
✨Emphasise Collaboration and Communication
This role requires working closely with data scientists and platform engineers, so be ready to discuss how you’ve successfully collaborated in Agile teams. Share examples of how clear communication has helped you achieve project goals and improve processes.