MLOPS Lead in York

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. #J-18808-Ljbffr