About the Role
The company is an end-to-end insurance operating system for MGAs, brokers, and insurers, powering elseco, a DFSA-regulated specialty MGA. We build full-stack applications on AWS and are now building a fully orchestrated AI platform: an AI-enabled ingestion and orchestration layer with agents working under human oversight, embedded as the company intellectual property.
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This role designs, builds, and operates the cloud and AI platform beneath those applications. It is a hands-on engineering role requiring the platform you build to be defined in code, secure by design, observable, resilient, cost-controlled, and audit-ready. Collaboration with application engineering (Node.js), data, architecture, and the Infrastructure and Cyber Security managers is essential.
Key Responsibilities
- Architect and build the AWS foundation for the company: accounts, networking, identity, encryption and shared services, all as reviewed, reproducible code.
- Build and operate the AI orchestration layer: workflow orchestration, Bedrock agents and retrieval, guardrails, evaluation pipelines, and human-review queues, starting with the aviation submissions pilot.
- Enable application teams with secure, paved-road patterns for containers, functions, APIs and eventing, ensuring engineers can ship without bespoke infrastructure work.
- Own CI/CD design and operation: pipelines, environment promotion, policy checks, secrets handling and release controls.
- Instrument everything: metrics, logs, traces and AI-specific telemetry, with alerting that detects issues before users do.
- Control cost: tagging, budgets, model and capacity choices, and monthly unit-cost reporting with a clear optimisation backlog.
- Design and test DR/BC for the platform with the Infrastructure and Cyber Security managers, driving action closure from each test.
- Maintain data-flow architecture from public entry points through application services, private networking, and data stores, ensuring it is current for incident triage, change impact analysis, and DFSA or audit requests.
- Make the platform independent of individuals through runbooks, standards, documentation, and knowledge transfer to the wider engineering team.
Requirements
- Proven design and operation of production AWS workloads across compute, orchestration, eventing, networking, and storage.
- At least 8 years in software, platform, or infrastructure engineering, with a minimum of 4 years designing and operating production workloads on AWS and demonstrable production experience running LLM or AI services.
- Expertise in infrastructure-as-code using Terraform or AWS CDK, ensuring no manual changes in production.
- Proficient in production-standard programming in Python and/or TypeScript/Node.js.
- Hands-on production experience with Amazon Bedrock or an equivalent LLM platform (e.g. Azure OpenAI, Vertex AI), including retrieval-augmented generation, guardrails, evaluation, and cost controls.
- Experience designing CI/CD pipelines with GitHub Actions or an equivalent tool.
- Knowledge of cloud security design: IAM, encryption, secrets management, and private network patterns.
- Strong observability engineering skills across metrics, logs, and distributed tracing.
- Experience with DR/BC, including RTO/RPO, restore testing, DR runbooks, and test execution.
- Solid Linux production operations knowledge.
- Ability to document data-flow architecture clearly for engineers, auditors, and regulators.
- Comfort operating in a regulated, audit-driven environment, with strong documentation and runbook discipline.
