Platform Engineer

Gibson Dunn is a leading global law firm, advising clients on significant transactions and disputes. Our exceptional teams craft and deploy creative legal strategies that are meticulously tailored to every matter, however complex or high-stakes. The firm’s work is distinguished by a unique combination of precision and vision.

Based in London, the Platform Engineer will be responsible for building the reusable platforms, tools, and services that improve developer productivity, accelerate delivery, and standardize engineering practices across the firm. The role writes and ships the code behind the firm’s internal developer platform, building and maintaining self-service tooling, infrastructure-as-code modules, pipeline definitions, and automation across CI/CD, cloud, and observability, and participating in the team’s operational and on-call duties.

Working under the guidance of senior engineers, this role delivers reliable, well-tested code and self-service capabilities for development teams while growing toward end‑to‑end ownership.

The platform supports conventional in-house software alongside AI workloads such as model inference, retrieval, and agent-based systems, all on a shared foundation. This role helps extend the same pipelines and reliability standards to AI workloads, while recognizing where AI demands different primitives such as evaluations, non‑deterministic failure handling, token economics, and confidentiality controls.

• CI/CD, Source Control & Test Automation:
  Git Hub Actions, Azure Dev Ops, Git Lab CI, Jenkins;
  Git, JFrog Artifactory;
  Playwright, pytest/JUnit
• Infrastructure & Config as Code:
  Terraform, Ansible, Bicep/ARM, Helm, Kustomize;
  Git Ops via Argo CD and Flux
• Cloud & Orchestration: AWS, Azure, Docker, Kubernetes
• AI Inference & Application Infrastructure:
  Frontier and open-weight models via Anthropic, Azure OpenAI, and Amazon Bedrock; model gateways and routing, retrieval and hybrid search, document ingestion, tool/function calling, Model Context Protocol (MCP), and agent orchestration
• AI Evaluation & Quality:
  Eval harnesses and golden datasets, LLM-as-judge and human-in-the-loop review, regression suites, and red-teaming
• Observability & Monitoring:
  Prometheus, Grafana, Datadog, Splunk, Elastic/ELK, Open Telemetry, including GenAI tracing and token, latency, and cost telemetry
• Platform Security & Policy-as-Code:
  Hashi Corp Vault, OPA/Conftest, SAST/DAST
• Developer Portal & Self-Service:
  Internal developer portal, CLIs/SDKs, and APIs

• Build and maintain self-service tooling, CLIs, libraries, and templates, as version-controlled, tested code, that make it easy for teams to build, test, and ship software.
• Contribute features and fixes to the internal developer platform and portal through pull requests and code review.
• Provide day-to-day support to developers using platform services, triaging and resolving requests and issues.
• Support governed self-service access to AI platform capabilities, including model access, retrieval tooling, and evaluation workflows, so AI capabilities can move from prototype to production using standard platform patterns.

• Implement and maintain pipelines-as-code (e.g., Git Hub Actions/Azure Dev Ops YAML) to established patterns, keeping builds, tests, and deployments reliable and fast.
• Execute and support release activities, following defined Git Ops and change-management processes.
• Write and maintain automated tests and quality gates within pipelines.
• Implement evaluation-based quality gates for AI systems, including evals-as-code, regression suites against golden datasets, and human-review thresholds where required.

• Author and maintain modular, tested infrastructure-as-code (e.g., Terraform, Helm) to provision and configure cloud and on-prem resources.
• Deploy and operate workloads across cloud platforms (AWS/Azure) and Kubernetes using Git Ops (Argo CD/Flux).
• Follow tagging, cost, and configuration standards when provisioning resources.
• Help deploy and operate AI workload…