MLOPS Lead

Job Description – ML Engineering Manager

• Position: ML Engineering Manager
• Reporting to: Head of Data Engineering
• Type: Permanent
• Band: II

Key Responsibilities

• Line Management of the ML Engineers, leading recruitment and onboarding of new engineers and identifying gaps in capacity and capability.
• Oversee the 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 encouraging coaching and mentoring of team members and supporting value stream management with partner resources.
• Influence key architectural decisions early on based on business, budgets and resiliency. Moving from a proof of concept 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.
• Identify bottlenecks and use engineering practices to improve processes.
• Turn business requirements into solution design diagrams and iterate on them.
• Break solution diagrams into deliverable pieces of work and milestones.
• Develop and maintain infrastructure for deploying ML models in real‑time and batch environments.
• Build and maintain Python APIs (Flask/Fast API) 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 development and improvement of the model registry, including tracking and implementation of model discontinuation upgrades and model monitoring.
• Own the deployment framework for all data science services.
• Oversee the automation of the data science life cycle (dataset build, training, evaluation, deployment, monitoring) when moving to production.
• Collaborate closely with data scientists, data engineers and other technical teams to support maturation of analytics practice.
• Write high‑quality Python code using industry best practice for model training and deployment.

Person Specification / Qualifications

• 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 challenges of migrating research code into production code.
• Hands‑on experience with GCP and machine learning engineering, including deploying, monitoring and maintaining ML models in production (neural networks, random forests, etc.).
• Experience in financial services or insurance with high regulation is an advantage but not required.
• Solid experience as a Python developer (Flask/Fast API, OOP, unit testing).
• Strong understanding of software engineering best practices.
• Experience with TDD.
• Experience with infrastructure‑as‑code tools like Terraform.
• Hands‑on experience with cloud platforms (GCP, AWS, or Azure).
• Familiarity with 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.
• Ability to articulate processes and tools used to ensure quality, stability, performance, scalability, deployment, security, and documentation.
• Creative, proactive, logical, and innovative; will push hard for innovation and automation.
• Highly results‑driven, with energy and determination to succeed in a fast‑paced environment.
• 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.
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