At a Glance
- Tasks: Lead and develop machine learning engineers while shaping scalable ML solutions.
- Company: Global insurance firm known for innovation and data-driven strategies.
- Benefits: Competitive salary, flexible working, generous leave, and professional growth opportunities.
- Other info: Join a diverse team committed to continuous improvement and innovation.
- Why this job: Influence technical strategy and mentor a dynamic team in a collaborative environment.
- Qualifications: Experience in machine learning engineering and team leadership required.
The predicted salary is between 80000 - 100000 £ per year.
Our client is a global organisation operating within the insurance and financial services sector, recognised for its commitment to innovation and the strategic use of data and technology. The business is investing heavily in modern data platforms, artificial intelligence, and advanced analytics to drive smarter decision-making and deliver value across its operations.
With a strong focus on collaboration and continuous improvement, the organisation brings together multidisciplinary teams spanning data science, engineering, and technology to build scalable, production-grade solutions. Employees are encouraged to contribute new ideas, develop their technical capabilities, and play an active role in shaping the organisation’s data-driven future.
Our client is seeking a Lead Machine Learning Engineer to shape and scale its machine learning engineering capability while ensuring the successful deployment and operation of machine learning solutions in production environments. This leadership role combines technical expertise with people management responsibilities, overseeing a team of Machine Learning Engineers while driving best practices across machine learning deployment, infrastructure, and MLOps. You will play a critical role in building scalable platforms, establishing engineering standards, and enabling teams to deliver robust, production-ready machine learning systems.
Working closely with data science teams, platform engineers, and senior stakeholders, you will ensure the organisation can efficiently move machine learning models from experimentation to reliable production systems. This role offers the opportunity to influence technical strategy, mentor engineers, and contribute to the development of enterprise-scale machine learning capabilities.
Key Responsibilities- Manage and develop Machine Learning Engineers, including setting objectives, conducting performance reviews, and supporting career progression.
- Foster a strong engineering culture that emphasises collaboration, quality, and operational excellence.
- Provide mentorship and coaching to support both technical and professional development.
- Define and evolve machine learning engineering strategy in alignment with organisational objectives.
- Establish engineering standards for machine learning deployment, infrastructure, and operational practices.
- Drive capability development across teams, including upskilling in MLOps, cloud platforms, and software engineering best practices.
- Lead the ownership and evolution of the organisation’s MLOps platform, ensuring reliability, scalability, and security.
- Enable scalable and reusable machine learning delivery across multiple business initiatives.
- Lead technical exploration activities such as proof-of-concepts and architectural investigations.
- Ensure machine learning systems comply with security, architecture, and operational standards.
- Establish guardrails for production machine learning systems, including monitoring, retraining, deployment, and lifecycle management.
- Partner closely with data science teams to ensure effective transition from experimentation to production deployment.
- Collaborate with platform and engineering teams to integrate machine learning solutions into enterprise systems.
- Represent machine learning engineering within strategic technology discussions and influence platform and tooling decisions.
- Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or another quantitative discipline, or equivalent practical experience.
- Significant experience as a Senior or Lead Machine Learning Engineer delivering machine learning systems in production environments.
- Strong understanding of machine learning and data science concepts, including supervised and unsupervised learning, feature engineering, and model evaluation techniques.
- Demonstrated experience leading or mentoring engineering teams, setting standards, and developing technical capabilities.
- Proven experience owning or managing MLOps platforms or critical machine learning infrastructure.
- Experience designing and implementing frameworks to evaluate the commercial impact of machine learning systems in production.
- Experience collaborating with data scientists throughout the end-to-end machine learning lifecycle.
- Strong communication skills and ability to work within Agile, cross-functional teams.
- Experience working within insurance, financial services, or other regulated industries.
- Experience implementing enterprise-scale machine learning platforms and governance frameworks.
- Exposure to advanced monitoring, incident management, and reliability practices for machine learning services.
- Python within a machine learning engineering context, including object-oriented programming, testing, and design patterns.
- Experience deploying, monitoring, and maintaining machine learning models in production systems.
- Cloud platforms such as AWS, Azure, or Google Cloud.
- Containerised deployments using Docker or similar technologies.
- MLOps practices, including CI/CD pipelines and Git-based development workflows.
- Infrastructure as Code tools such as Terraform.
- Experience with API operations, monitoring, logging, and reliability management.
- Strong working knowledge of SQL and data integration across application ecosystems.
Competitive salary and performance-based incentives. Pension contributions. Generous annual leave allowance. Flexible and hybrid working arrangements. Professional development and leadership growth opportunities. Collaborative and innovative technical environment. Opportunity to shape enterprise-scale machine learning engineering capabilities.
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.
Lead ML Engineer - Hybrid, Flexible, Growth & Perks in London employer: SPG Resourcing
Our client is an exceptional employer, offering a dynamic and collaborative work culture that prioritises innovation and professional growth. With flexible hybrid working arrangements and a strong commitment to employee development, this global insurance firm empowers its team members to shape the future of machine learning engineering while enjoying competitive benefits and a supportive environment.
StudySmarter Expert Advice🤫
We think this is how you could land Lead ML Engineer - Hybrid, Flexible, Growth & Perks in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people 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. Whether it's GitHub repos or a personal website, having tangible examples of your work can really set you apart from the crowd.
✨Tip Number 3
Prepare for those interviews! Brush up on your technical knowledge and be ready to discuss your past experiences. Practising common interview questions can help you feel more confident and articulate during the real deal.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got loads of opportunities waiting for you, and applying directly can sometimes give you a better chance at landing that dream role.
We think you need these skills to ace Lead ML Engineer - Hybrid, Flexible, Growth & Perks in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that align with the Lead ML Engineer role. Highlight your experience in machine learning, team leadership, and any relevant projects you've worked on.
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 innovative culture. Share specific examples of your achievements and how they relate to the job description.
Showcase Your Technical Skills:Don’t forget to mention your technical expertise, especially in Python, MLOps, and cloud platforms. We want to see how you’ve applied these skills in real-world scenarios, so be specific!
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 updates!
How to prepare for a job interview at SPG Resourcing
✨Know Your Stuff
Make sure you brush up on your machine learning concepts, especially those relevant to the insurance and financial services sector. Be ready to discuss supervised and unsupervised learning, feature engineering, and model evaluation techniques. This will show that you’re not just a leader but also technically savvy.
✨Showcase Your Leadership Skills
Prepare examples of how you've successfully managed and developed teams in the past. Highlight your experience in mentoring engineers and fostering a strong engineering culture. This role is about leading others, so demonstrating your ability to inspire and guide a team is crucial.
✨Align with Their Vision
Understand the company’s goals around innovation and data-driven decision-making. Be ready to discuss how you can contribute to their machine learning engineering strategy and help shape their data-driven future. Showing that you’re aligned with their objectives will make you a more attractive candidate.
✨Ask Smart Questions
Prepare thoughtful questions about their MLOps platform, engineering standards, and how they integrate machine learning solutions into their systems. This not only shows your interest in the role but also your understanding of the complexities involved in deploying machine learning in production environments.
