In addition to strong engineering skills, you’ll bring a solid understanding of Data Science principles. You should be comfortable reading, questioning, and interpreting machine learning models to ensure they are deployed appropriately and effectively. Your ability to bridge the gap between model development and production deployment will be key to delivering robust, high impact machine learning solutions. You’ll be expected to understand and implement methodologies from the ML OPs life cycle.
You’ll also be expected to work in an Agile environment, contributing to iterative development cycles, collaborating across disciplines, and adapting quickly to changing requirements.
Key Responsibilities
Line Management of the ML Engineers, leading the recruitment and onboarding of new engineers when relevant and identifying gaps in capacity and capability.
Oversee your team’s deployment of ML capabilities and provide support to the Head of Data Engineering, specifically around capacity and delivery of the portfolio.
As a Team Lead there is an expectation of coaching and mentoring your team members - and supporting the Head of Data Engineering in terms of overall value stream management - especially with partner resources.
Influence key architectural decisions early on based on requirements of the business, budgets and resiliency. From there working within the values streams to realise this. Moving from a POC to a production ready platform.
Coach, mentor and influence ML Engineers into greater ML maturity
Experience building a platform as a service product on top of cloud architecture
Identifying bottlenecks and using engineering practices to improve the processes
Taking business requirements and turning it in to a solution design diagram and iterating on it
Taking a solutions diagram and breaking that down in to delivarable pieces of work and milestones
Develop and maintain infrastructure for deploying ML models in both real-time and batch environments.
Build and maintain Python APIs (Flask/FastAPI) to serve ML models.
Collaborate with cross discipline engineers to integrate ML services into user-facing applications.
Work with platform engineers to align with infrastructure best practices and ensure scalable deployments.
Review pull requests and contribute to code quality across the MLE team.
Monitor and maintain cloud-based ML services, ensuring reliability and performance.
Design and implement CI/CD pipelines for ML model deployment.
Write unit tests and follow object-oriented programming principles to ensure maintainable code.
Support data modelling and cloud networking tasks as needed.
Contribute to the development and improvement to our model registry, including tracking and implementation of model discontinuation upgrades and model monitoring.
Ownership of the deployment framework for all data science services. You will have oversight of how data will flow into the data science life cycle from the wider business data warehouse
Oversight of the automation of the data science life cycle (dataset build, training, evaluation, deployment, monitoring) when we move to production
Interest and ability to work closely with a team and collaborate on all aspects of the data science and deployment lifecycle
Work collaboratively with data scientists, data engineers and other technical teams in order to help support maturation of analytics practice within the organization
Writing high quality python code using industry best practice for model training and deployment
Person Specification
To succeed in this role, you’ll typically have
Bachelor's/Master's degree in a quantitative field (e.g., Computer Science, Statistics, Mathematics, Physics, Engineering) or equivalent.
5+ years as an ML engineer
Good understanding of core data science principles and understanding of challenges of migrating research code into production code
Hands on experience of GCP and machine learning engineering, including deploying, monitoring, and maintaining ML models in production environments (Neural networks, Random forests etc.)
Experience in financial services or insurance with high amounts of regulation is an advantage but not required.
Solid experience as a Python developer, ideally in a machine learning engineering context (Flask/FastAPI, OOP, unit testing)
Strong understanding of software engineering best practice.
Experience with TDD.
Experience with infrastructure as code tools like Terraform or similar Infrastructure as Code (IaC) tools
Hands on experience with cloud platforms (GCP, AWS, or Azure).
Familiarity with containerization using Docker and orchestration of deployments.
Experience with CI/CD tools and Git-based development workflows.
Understanding of API operations monitoring and logging.
Strong problem-solving skills and ability to work independently on technical tasks.
Familiarity with Agile methodologies and experience working in Agile teams.
Able to articulate on processes and tools utilised to ensure quality, stability, performance, scalability, deployment, security, maintenance and documentation.
Creative, proactive, logical and innovative – you do not accept the status quo – and will push hard for innovation and automation.
Highly results driven, with the energy and determination to succeed in a very fast paced environment where the pace of response is critical to success.
Ability to work as part of a small team that is part of a larger product division
Proven communication and presentation skills
Comfortable in a rapidly changing environment
Work with amazing people and be part of a unique culture
