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- Tasks: Lead a team of ML Engineers to develop and deploy innovative machine learning solutions.
- Company: Join a thriving global financial services organisation on a transformative journey.
- Benefits: Enjoy hybrid/remote work, competitive salary, and opportunities for professional growth.
- Why this job: Make a real impact in revolutionising data processing and monetising insights.
- Qualifications: 5+ years in ML engineering with strong Python skills and cloud experience.
- Other info: Dynamic, collaborative environment with a focus on innovation and automation.
The predicted salary is between 80000 - 100000 £ per year.
TeamCandour are working with a thriving, global financial services organisation to onboard a passionate and experienced Machine Learning (ML) Lead / Manager to head up a newly formed ML Engineering team. This is a unique opportunity to join a mission-driven organisation on a rocket ship trajectory as part of a 4 year transformation programme to revolutionise the way they process & monetise the data they hold with a view to doubling their overall global revenue.
As the ML Engineering Manager, you will:
- Lead and manage a team of ML Engineers, including recruitment, onboarding, coaching, and mentoring.
- Oversee the deployment of ML capabilities and support the Head of Data Engineering in capacity planning and portfolio delivery.
- Influence architectural decisions to ensure scalable, resilient, and cost-effective solutions.
- Develop and maintain infrastructure for deploying ML models in real-time and batch environments.
- Build and maintain Python APIs (Flask/FastAPI) to serve ML models.
- Collaborate with cross-functional teams, including Data Scientists, Platform Engineers, and Developers, to integrate ML services into user-facing applications.
- Design and implement CI/CD pipelines for ML model deployment.
- Monitor and maintain cloud-based ML services to ensure reliability and performance.
- Contribute to the development and improvement of the model registry, including tracking, upgrades, and monitoring.
- Drive the automation of the data science lifecycle, from dataset creation to model deployment and monitoring.
- Advocate for and implement software engineering best practices, including test-driven development (TDD), object-oriented programming (OOP), and infrastructure as code (IaC).
To excel in this role, you should have:
- A Bachelor's or Master's degree in a quantitative field (e.g., Computer Science, Statistics, Mathematics, Physics, Engineering) or equivalent experience.
- 5+ years of experience as an ML Engineer, with hands-on expertise in deploying, monitoring, and maintaining ML models in production environments.
- Strong understanding of core data science principles and the challenges of transitioning research code to production.
- Proficiency in Python development, particularly in a machine learning engineering context (Flask/FastAPI, OOP, unit testing).
- Experience with GCP (Google Cloud Platform) and familiarity with other cloud platforms like AWS or Azure.
- Knowledge of containerization (Docker) and orchestration tools.
- Experience with CI/CD tools and Git-based development workflows.
- Familiarity with Agile methodologies and experience working in Agile teams.
- Strong problem-solving skills, creativity, and a proactive approach to innovation and automation.
- Excellent communication and presentation skills.
Your typical day will involve:
- Leading and mentoring your team of ML Engineers to deliver high-quality, scalable solutions.
- Collaborating with Data Scientists, Platform Engineers, and Developers to design and implement ML services.
- Writing clean, reusable Python code and reviewing pull requests to ensure code quality.
- Designing and maintaining CI/CD pipelines for seamless model deployment.
- Monitoring and optimizing cloud-based ML services for performance and reliability.
- Translating business requirements into solution designs and actionable tasks.
- Driving the automation of the data science lifecycle to enhance operational efficiency.
- Participating in Agile development cycles and adapting to evolving project requirements.
Curious? We're always available to talk through what could be the next ideal move in your career trajectory, drop us a line anytime!
ML Engineering Lead in York employer: Candour
Contact Detail:
Candour Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land ML Engineering Lead in York
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with potential colleagues on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your ML projects, GitHub contributions, or any relevant work. This gives hiring managers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on common ML engineering questions and scenarios. Practice explaining your thought process and problem-solving approach, as this will help you shine during technical discussions.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for passionate individuals to join our team. Plus, it’s a great way to ensure your application gets the attention it deserves.
We think you need these skills to ace ML Engineering Lead in York
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the ML Engineering Lead role. Highlight your experience with Python, cloud platforms, and any leadership roles you've had. 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 tell us why you're passionate about machine learning and how you can contribute to our mission. Be genuine and let your personality come through.
Showcase Your Projects: If you've worked on relevant projects, don't hold back! Include links to your GitHub or any other portfolio showcasing your work with ML models, APIs, or CI/CD pipelines. We love seeing practical examples of your skills.
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 the role. Plus, it makes the process smoother for everyone involved!
How to prepare for a job interview at Candour
✨Know Your Stuff
Make sure you brush up on your machine learning principles and the specific technologies mentioned in the job description. Be ready to discuss your hands-on experience with deploying ML models, especially in Python using Flask or FastAPI. This will show that you’re not just familiar with the theory but have practical skills to back it up.
✨Showcase Your Leadership Skills
As a potential ML Engineering Lead, it's crucial to demonstrate your leadership abilities. Prepare examples of how you've successfully managed teams, mentored junior engineers, or influenced architectural decisions in past roles. This will help the interviewers see you as a strong candidate who can lead their new ML team effectively.
✨Be Ready for Technical Questions
Expect technical questions that dive deep into your experience with CI/CD pipelines, cloud platforms like GCP, and containerization tools like Docker. Practise explaining complex concepts in simple terms, as this will showcase your communication skills and ability to collaborate with cross-functional teams.
✨Ask Insightful Questions
Prepare thoughtful questions about the company's ML transformation programme and how they envision the role evolving. This shows your genuine interest in the position and helps you gauge if the company culture aligns with your values. Plus, it’s a great way to engage with your interviewers!