Job Description
Machine Learning (ML) Lead / Manager - York (hybrid / remote)
#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 programmer 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!