Job Description – ML Engineering Manager
Position: ML Engineering Manager
Reporting to: Head of Data Engineering
Location: York or Lisbon
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/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 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/FastAPI, 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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