Machine Learning (ML) Lead / Manager - Lisbon (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!