Machine Learning Manager

SPG Resourcing; York and North Yorkshire; Hybrid; GBP 70,000 - 90,000; Full time

Job summary

SPG Resourcing is seeking a Machine Learning Engineering Manager to lead a new ML Engineering team in York or London. This role involves building scalable ML infrastructure and managing the end-to-end MLOps lifecycle. Ideal candidates will have over 5 years of experience in machine learning with a strong background in production deployments, especially on GCP Vertex AI. The position is hybrid, offering flexibility, alongside a competitive salary and benefits package.

Qualifications

• 5+ years’ experience as a Machine Learning Engineer with strong production deployment background.
• Hands-on expertise with GCP (Vertex AI) and cloud-based ML model deployment.
• Strong understanding of MLOps practices and challenges in production.

Responsibilities

• Lead and manage the ML Engineering team including recruitment and training.
• Build and maintain scalable ML infrastructure on cloud platforms.
• Own the end-to-end MLOps lifecycle from data ingestion to model deployment.

Skills

Production deployment experience

GCP (Vertex AI)

MLOps practices

Infrastructure as Code (Terraform)

Docker

CI/CD

Git workflows

Stakeholder management

Agile environment

Job description

Job Title: Machine Learning Engineering Manager

Location: London OR York (Hybrid)

Salary: Competitive + Benefits

Type: Permanent

About the Company

We are a growing, data-driven organisation investing significantly in our data and analytics capabilities as part of a major strategic transformation. With a strong focus on leveraging advanced analytics and machine learning to drive business performance, we are building scalable, production-grade ML platforms and maturing our data science practice across the enterprise.

The Role

This is a hands‑on leadership role for a Machine Learning Engineering Manager who combines strong technical expertise with the ability to lead and develop a team.

You will lead a newly formed ML Engineering team, building and maintaining the infrastructure and platforms required to deploy, monitor, and scale machine learning models in production. Working closely with Data Scientists, Platform Engineers, and cross‑functional stakeholders, you will bridge the gap between model development and enterprise deployment, ensuring robust, reliable, and high‑impact ML solutions.

This is an exciting opportunity to shape and build the ML engineering function from the ground up, rather than stepping into a fully established team.

Key Responsibilities

• Lead and line manage the ML Engineering team, including recruitment, onboarding, and capability development
• Build and maintain scalable ML infrastructure and deployment pipelines on cloud platforms (GCP Vertex AI essential, Azure desirable)
• Design, develop and own Python APIs (Flask/FastAPI) and services to serve machine learning models in real‑time and batch environments
• Own the end‑to‑end MLOps lifecycle – from data ingestion through to model deployment, monitoring, and automation
• Translate business requirements into technical solution designs and deliver them from proof‑of‑concept to production
• Influence architectural decisions with a focus on scalability, resiliency, and cost‑effectiveness
• Coach and mentor ML Engineers to raise technical maturity and best practices across the team
• Collaborate with Data Scientists, Data Engineers, and Platform teams to integrate ML solutions into business applications
• Implement CI/CD pipelines, Infrastructure as Code, monitoring, and model registry processes
• Drive operational excellence, code quality, and continuous improvement of ML platforms and processes
• Support the Head of Data Engineering on portfolio delivery, capacity planning, and value stream management

Skills & Experience Required

• 5+ years’ experience as a Machine Learning Engineer with strong production deployment background
• Hands‑on expertise with GCP (Vertex AI) and cloud‑based ML model deployment and monitoring
• Strong understanding of MLOps practices and the challenges of moving models from research to production
• Experience with Infrastructure as Code (Terraform or similar), Docker, CI/CD, and Git workflows
• Good knowledge of core data science concepts (neural networks, random forests, etc.) and ability to review/interpret models
• Proven experience leading or mentoring technical teams
• Excellent stakeholder management and communication skills
• Ability to operate in a fast‑paced, Agile environment and drive change
• Experience in financial services or a regulated industry is advantageous but not essential

If this sounds like something you are interested in, please get in contact. [e-mail]

SPG Resourcing is an equal opportunities employer and is committed to fostering an inclusive workplace which values and benefits from the diversity of the workforce we hire. We offer reasonable accommodation at every stage of the application and interview process.