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- Tasks: Lead a dynamic ML Engineering team and build scalable machine learning platforms.
- Company: A forward-thinking, data-driven organisation focused on advanced analytics.
- Benefits: Competitive salary, hybrid work model, and opportunities for professional growth.
- Other info: Join a collaborative environment that values diversity and innovation.
- Why this job: Shape the future of ML engineering and make a real impact in a growing field.
- Qualifications: 5+ years in machine learning with strong deployment experience and leadership skills.
The predicted salary is between 70000 - 90000 ÂŁ per year.
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. Thomas.deakin@spgresourcing.com
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.
Machine Learning Manager employer: SPG Resourcing
Contact Detail:
SPG Resourcing Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Manager
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with potential colleagues on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects, especially those that highlight your experience with GCP and MLOps. This will give you an edge and demonstrate your hands-on expertise.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and soft skills. Be ready to discuss your experience leading teams and how you've tackled challenges in deploying ML models. Practice makes perfect!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Machine Learning Manager
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that match the Machine Learning Manager role. Highlight your hands-on experience with GCP and MLOps practices, as well as any leadership roles you've held.
Craft a Compelling Cover Letter: Use your cover letter to tell us why you're passionate about machine learning and how you can contribute to our team. Share specific examples of your past achievements in ML engineering and team management.
Showcase Your Technical Skills: Don’t shy away from detailing your technical expertise! Mention your experience with Python APIs, CI/CD pipelines, and any relevant cloud platforms. We want to see how you can bridge the gap between model development and deployment.
Apply Through Our Website: We encourage you to apply directly through our website for a smoother application process. It helps us keep track of your application and ensures you don’t miss out on any important updates!
How to prepare for a job interview at SPG Resourcing
✨Know Your Tech Inside Out
Make sure you brush up on your technical skills, especially around GCP Vertex AI and MLOps practices. Be ready to discuss your hands-on experience with deploying machine learning models and how you've tackled challenges in production environments.
✨Showcase Your Leadership Skills
Since this role involves leading a team, be prepared to share examples of how you've successfully managed or mentored others. Highlight your approach to building a collaborative environment and how you've influenced architectural decisions in past projects.
✨Understand the Business Context
Familiarise yourself with the company's goals and how machine learning can drive business performance. Be ready to translate technical solutions into business value, demonstrating your ability to bridge the gap between tech and strategy.
✨Prepare for Scenario-Based Questions
Expect questions that assess your problem-solving skills in real-world scenarios. Think about specific challenges you've faced in ML deployment and how you overcame them, as well as your approach to continuous improvement and operational excellence.