Senior AI Engineer (Remote) in London

This is your opportunity to join AXIS Capital – a trusted global provider of specialty lines insurance and reinsurance. We stand apart for our outstanding client service, intelligent risk taking and superior risk adjusted returns for our shareholders. We also proudly maintain an entrepreneurial, disciplined and ethical corporate culture. All qualified applicants will receive consideration for employment without regard to any protected characteristic, including age, color, disability, ethnicity, gender identity, marital status, national origin, pregnancy, race, religion, sex, sexual orientation, veteran status, or any basis prohibited by the laws that govern its operations. Senior AI Platform EngineerHow does this role contribute to our collective success? The Senior AI Platform Engineer will help AXIS deliver secure, reliable, and reusable AI capabilities that support business teams across underwriting, claims, operations, and other strategic functions. This role sits at the intersection of platform engineering, DevOps, data engineering, and applied AI, with a focus on productionising AI services safely and consistently. You will design, build, deploy, and operate cloud-native AI workloads, including LLM-powered services, agentic workflows, data pipelines, and reusable platform components. You will play a key role in improving engineering standards, automation, observability, release discipline, and operational readiness across AXIS AI platforms. This is a hands-on senior engineering role for someone who can take ownership from design through production support, work effectively with distributed teams, and help shape pragmatic, maintainable approaches to enterprise AI delivery. Design, build, and operate production-grade AI services, data pipelines, and platform capabilities on Azure and Databricks. Develop LLM-powered and agentic workflows that support business process automation, document intelligence, orchestration, and decision support. Expose AI capabilities through secure, maintainable APIs and Azure Function or Container App backend services, including FastAPI-based or other services where appropriate. Own CI/CD delivery pipelines for AI and data workloads, including build, release, deployment, validation, and environment promotion. Build and manage Databricks jobs, workflows, asset bundles, and data engineering pipelines. Implement monitoring, logging, tracing, and alerting using OpenTelemetry or equivalent observability tools. Support production AI systems, including incident response, troubleshooting, operational runbooks, release readiness, and service improvement. Collaborate with engineering, data science, product, security, infrastructure, and business teams to deliver end-to-end solutions. Required skills and experienceStrong production-level Python engineering experience, with the ability to build maintainable, tested, and supportable services. Experience with PySpark or equivalent large-scale data processing frameworks. Hands-on experience designing and operating CI/CD pipelines using Azure DevOps or similar tooling. Experience deploying and supporting workloads on Azure, including identity, access control, networking, and secure resource configuration. Experience building APIs, backend services, and service integrations for data or AI workloads. Experience with monitoring, logging, alerting, incident response, and operational support for production systems. Strong engineering discipline across testing, code quality, branching, release management, and automated validation. Ability to troubleshoot across application, data, infrastructure, deployment, and security boundaries. Desirable skills and experienceExperience with Databricks Jobs, Workflows, Asset Bundles, Apps, and medallion-style data architectures. Experience with Azure AI Foundry, Azure OpenAI, LLM-based application development, or agent SDKs and frameworks. Experience with MLflow, model lifecycle management, evaluation, tracing, and AI observability. Experience building reusable platform components, shared libraries, templates, or engineering standards. What good looks likeAI services are delivered with clear ownership from design through deployment and production support. Incidents, risks, and technical dependencies are surfaced early, communicated clearly, and resolved collaboratively. Role factorsThis role requires close collaboration with distributed technology, business, data, security, infrastructure, and vendor teams. The candidate should be comfortable engaging with stakeholders, explaining technical decisions clearly, and supporting local and global team objectives. Halifax, NSType: Full time