Principal Machine Learning Engineer

Job Type: Permanent.

Build a brilliant future with the company.

Location: London / York

Why the company London Market:

The company London Market sits at the centre of global specialist insurance, tackling some of the most complex and unusual risks in the world. These are not commoditised problems; they demand deep expertise, strong judgement, and increasingly, sophisticated data and machine learning capabilities. We have a strong track record of putting AI into real production use, from augmenting underwriting decisions to shaping future market standards through partnerships and market‑first innovation. This is an environment where advanced ML systems are expected to operate reliably, safely, and at scale, not remain in experimentation. You’ll join a culture that values technical excellence, ownership, and courage, where senior individual contributors are trusted to set direction, challenge thinking, and build platforms that matter.

Role Purpose:

As a Principal Machine Learning Engineer (MLE), you bring a wealth of experience in building, scaling, and operating production machine learning systems, and use that experience to provide deep technical leadership across machine learning engineering and MLOps. You play a key role in shaping the architectural strategy for production ML systems and the ML Platform, working closely with Data Science, Engineering, and Platform teams to define patterns, standards, and tooling that enable reliable, repeatable delivery at scale. Through hands‑on contribution, design leadership, and technical mentorship, you help teams navigate complex technical decisions and build robust, maintainable systems. A central focus of the role is enabling the organisation to move quickly without sacrificing quality, evolving the ML platform, supporting the transition from experimentation to production, and helping teams adopt modern engineering practices. This includes championing the effective and responsible use of AI‑assisted development tools as part of a broader approach to improving developer experience, system quality, and long‑term sustainability. Success in this role comes from the practical application of deep experience, strong architectural thinking, and the ability to help others build better systems that deliver real business value.

Key Responsibilities:

• Technical Leadership & Ownership (Individual Contributor): Act as the technical lead for Machine Learning Engineering and MLOps across London Market. Technically lead the most complex and business‑critical ML systems, from architectural design through to production operation. Define and evolve production ML patterns and best practices, covering deployment, orchestration, monitoring, retraining, and decommissioning. Lead deep technical decision‑making, balancing scalability, reliability, security, and developer experience. Contribute hands‑on to critical systems, frameworks, and platform components where the complexity or impact demands it. Define best practices and guardrails for the use of AI‑assisted coding tools, ensuring they are used to improve productivity and code quality without compromising maintainability or operational safety. Lead by example in applying AI‑assisted development techniques (e.g. for prototyping, refactoring, and accelerating complex engineering work) with strong engineering judgement.
• ML Platform Strategy & Build‑Out: Partner closely with Group and Platform teams to design, build, and evolve the ML Platform, ensuring it supports reusable and scalable deployment patterns, CI/CD for machine learning, full model lifecycle management, monitoring, observability, and alerting, and secure and compliant operation. Shape platform standards and interfaces that enable consistent ML delivery across squads and value streams. Lead technical spikes and proof‑of‑concepts to evaluate new tools, approaches, and architectural patterns. Influence developer tooling choices so that AI‑assisted coding tools integrate safely with CI/CD, testing, and governance workflows. Ensure the platform enables fast, safe experimentation while supporting robust, long‑lived production systems.
• Governance, Reliability & Commercial Impact: Ensure ML systems meet architecture, security, compliance, and operational standards. Define and implement robust frameworks for monitoring technical health, model performance, and commercial impact of ML systems in production. Champion operational excellence across ML services, including monitoring, alerting, incident management, and post‑incident learning. Ensure clear ownership and lifecycle management for models and ML‑backed services. Promote responsible use of automation and AI‑assisted tooling in safety‑critical or regulated contexts.
• Collaboration & Influence: Work closely with the Data Science team to ensure a smooth and repeatable transition from experimentation to production. Collaborate with software engineers, product managers, and business stakeholders to deliver end‑to‑end ML‑driven solutions. Act as a senior technical voice in design reviews, architecture forums, and strategic discussions. Influence technical direction and standards through expertise, credibility, and collaboration rather than line management.
• Technical Mentorship & Capability Development: Provide hands‑on technical mentorship to Machine Learning Engineers and Data Scientists. Raise engineering standards by sharing best practices, patterns, and lessons learned from real production systems. Coach teams on the effective and critical use of AI‑assisted coding tools, reinforcing the importance of code review discipline, testing, and long‑term maintainability. Contribute to technical hiring, assessment, and onboarding from a senior engineering perspective. Help shape long‑term capability by identifying gaps in tooling, skills, and platform maturity.

What You’ll Bring:

• Experience & Background: Bachelor’s or Master’s degree in Computer Science, Engineering, or a related quantitative field (or equivalent experience). Extensive experience as a senior or principal Machine Learning Engineer delivering production ML systems at scale. Proven track record of owning or shaping ML platforms, MLOps frameworks, or critical ML infrastructure. Experience operating in complex, cross‑functional environments (insurance or financial services experience is advantageous but not essential).
• Technical Expertise: Exceptional Python skills in a machine learning engineering context, with strong software engineering fundamentals (OOP, testing, design patterns). Deep experience building, deploying, and operating production ML systems, including online and batch model serving, monitoring, alerting, and observability, and retraining and lifecycle management. Strong understanding of core data science concepts, sufficient to review and challenge modelling approaches and ensure models are production‑ready and correctly evaluated. Hands‑on experience with a major cloud platform (AWS, GCP, or Azure), including containerised deployments. Expert knowledge of MLOps and CI/CD, including Git‑based workflows, Infrastructure as Code (e.g. Terraform), and automated testing and deployment pipelines. Strong operational mindset, including APIs, logging, monitoring, reliability, and incident response. Working knowledge of SQL and integration of ML services into wider data and application ecosystems. Experience using AI‑assisted coding tools in a production engineering context, with a clear understanding of their benefits, limitations, and risks.

Why Join Us?

This is an opportunity to shape the future of machine learning engineering at the company, build a high‑performing sub‑chapter, and influence strategic decisions, while staying close to the craft you love. You’ll have the autonomy to set standards, mentor talent, and explore emerging technologies, all within a collaborative and forward‑thinking environment. Work with amazing people and be part of a unique culture.