MLOps Engineer

Description

Permanent role in the Group and Enterprise Services (GES) function, led by the CTO for GES, which owns the full life cycle of about 140 applications.

GES is organised into service verticals: Business Applications (six value streams plus an Enterprise Application team), Data, End User Experience, Core Engineering, Architecture and Portfolio Management.

You will sit in the Data vertical and report to the Head of Data Engineering.

You will lead a newly formed machine learning engineering team as a hands-on engineering manager, focused on APIs, infrastructure and deployment pipelines for ML models.

You will build and run the infrastructure that pulls data from the data platform, deploys models, and monitors and upgrades core data science services on GCP Vertex AI (essential) and Azure (desirable), supporting ML deployment across the enterprise.

You will work with data scientists, platform engineers and developers to deliver scalable, production-grade ML solutions and apply MLOps practices across the life cycle. You should be able to read, question and interpret models so they are deployed correctly.

Work is Agile, with iterative delivery and quick adaptation to changing requirements.

Responsibilities

• Hire, line manage and onboard ML engineers, and identify gaps in capacity and skills.
• Coach, mentor and push the team toward greater ML maturity.
• Oversee the team's delivery of ML capabilities, and support the Head of Data Engineering on capacity and portfolio delivery, including partner resources and value stream management.
• Shape key architecture decisions early, weighing business needs, budget and resilience, then take the platform from proof of concept to production.
• Turn business requirements into solution design diagrams, iterate on them, and split them into deliverable pieces and milestones.
• Find bottlenecks and improve processes using engineering practice.
• Build a platform-as-a-service product on top of cloud architecture.
• Develop and maintain infrastructure for real-time and batch model deployment.
• Build and maintain Python APIs in Flask or FastAPI to serve models.
• Design and implement CI/CD pipelines for model deployment.
• Work with cross-discipline engineers to bring ML services into user-facing applications, and with platform engineers on infrastructure best practice and scalable deployments.
• Monitor and maintain cloud ML services for reliability and performance.
• Review pull requests and raise code quality across the MLE team.
• Own the deployment framework for all data science services, including how data flows in from the business data warehouse.
• Oversee automation of the data science life cycle: dataset build, training, evaluation, deployment and monitoring.
• Contribute to the model registry, including tracking, discontinuing and upgrading models, and monitoring them.
• Support data modelling and cloud networking as needed.
• Write high-quality Python for model training and deployment, with unit tests and object-oriented design.

Requirements

• Bachelor's or Master's degree in a quantitative field such as computer science, statistics, mathematics, physics or engineering, or equivalent experience.
• 5+ years as an ML engineer.
• Solid grasp of core data science principles and of the difficulty of moving research code into production.
• Hands-on GCP and ML engineering experience, including deploying, monitoring and maintaining production models such as neural networks and random forests.
• Solid Python development, ideally in ML engineering, including Flask or FastAPI, OOP and unit testing.
• Strong grasp of software engineering best practice and experience with TDD.
• Experience with infrastructure as code such as Terraform or similar.
• Hands-on experience with a cloud platform (GCP, AWS or Azure).
• Familiarity with Docker containers and orchestrating deployments.
• Experience with CI/CD tools and Git-based workflows.
• Understanding of API monitoring and logging.
• Strong problem solver who can work independently on technical tasks.
• Familiarity with Agile methods and experience working in Agile teams.
• Able to explain the processes and tools used for quality, stability, performance, scalability, deployment, security, maintenance and documentation.
• Creative, proactive and logical, challenging the status quo and driving innovation and automation.
• Results-driven and determined, comfortable in a busy environment where quick response matters.
• Able to work within a small team inside a larger product division.
• Proven communication and presentation skills.
• Comfortable with rapid change.
• Experience in financial services or insurance, with heavy regulation, is an advantage but not required.

Conditions

Permanent contract.

Agile working with iterative delivery cycles.