Senior Ai Engineer

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Senior AI Platform Engineer

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, Dev Ops, data engineering, and applied AI, with a focus on product ionising 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.
• Implement infrastructure-as-code using Terraform and Azure-native tooling, ensuring environments are repeatable, secure, and supportable.
• Build and manage Databricks jobs, workflows, asset bundles, and data engineering pipelines.
• Implement monitoring, logging, tracing, and alerting using Open Telemetry or equivalent observability tools.
• Support production AI systems, including incident response, troubleshooting, operational runbooks, release readiness, and service improvement.
• Contribute to architecture decisions, engineering standards, reusable patterns, and platform strategy.
• Collaborate with engineering, data science, product, security, infrastructure, and business teams to deliver end-to-end solutions.
• Mentor engineers, lead technical enablement sessions, and promote strong engineering practices across the team.

• Strong 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 Dev Ops or similar tooling.
• Experience deploying and supporting workloads on Azure, including identity, access control, networking, and secure resource configuration.
• Practical experience with infrastructure-as-code, preferably Terraform.
• 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.
• Strong communication skills, with the ability to work with technical and non-technical stakeholders.

• Experience 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 with containers, Docker, Kubernetes, AKS, Open Shift, or equivalent container platforms.
• Experience building reusable platform components, shared libraries, templates, or engineering standards.
• Experience working in regulated environments with strong security, governance, auditability, and operational control requirements.

• AI services are delivered with clear ownership from design through deployment and production support.
• Delivery pipelines are automated, repeatable, secure, and aligned to release management standards.
• Systems are observable, supportable, and designed with…