Lead Machine Learning Engineer

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.

Strategic Capability 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.

Technical Enablement & Platform Ownership

• 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.

Governance & Standards

• 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.

Qualifications and Skills Required

• 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.

Preferred

• 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.

Key Technical Skills

• 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.

Equal Opportunity Statement

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.