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 firms 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 firms 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 teams 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.

Primary applications and platforms include:

• CI/CD Source Control & Test Automation: GitHub Actions Azure DevOps GitLab CI Jenkins; Git JFrog Artifactory; Playwright pytest/JUnit
• Infrastructure & Config as Code: Terraform Ansible Bicep/ARM Helm Kustomize; GitOps 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 OpenTelemetry including GenAI tracing and token latency and cost telemetry
• Platform Security & Policy-as-Code: HashiCorp Vault OPA/Conftest SAST/DAST
• Developer Portal & Self-Service: Internal developer portal CLIs/SDKs and APIs

Responsibilities include:

Developer Experience & Self-Service Enablement

• 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.

CI/CD Release Management & Test Automation

• Implement and maintain pipelines-as-code (e.g. GitHub Actions/Azure DevOps YAML) to established patterns keeping builds tests and deployments reliable and fast.
• Execute and support release activities following defined GitOps 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.

Infrastructure as Code & Cloud Platforms

• 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 GitOps (Argo CD/Flux).
• Follow tagging cost and configuration standards when provisioning resources.
• Help deploy and operate AI workload infrastructure including model gateways retrieval services orchestration components and supporting cloud or Kubernetes resources.

Observability Monitoring & Site Reliability (SRE)

• Instrument services and implement monitoring logging and alerting as code using standard tooling (Prometheus Grafana OpenTelemetry).
• Participate in the on-call rotation responding to incidents and helping restore service.
• Contribute to blameless post-incident reviews and implement follow-up actions in code.
• Instrument AI services for operational visibility including tracing across prompts tools and agent steps and monitoring latency cost token usage and quality regressions.

Platform Security & Automation

• Apply platform security controls and remediate vulnerabilities to defined standards including policy-as-code checks (e.g. OPA/Conftest).
• Manage secrets access and configuration securely using approved tooling (e.g. Vault).
• Apply security and confidentiality controls for AI workloads including prompt-injection and data-exfiltration defenses output filtering and access controls across prompts retrieval and agent tool use.
• Automate repetitive operational tasks by writing scripts and small services.

Software Engineering Standards & Collaboration

• Follow engineering standards version-control workflows code-review and testing practices.
• Collaborate with platform and development teams and escalate complex issues appropriately.
• Create and maintain runbooks SOPs and documentation as code alongside the tooling it describes.

Qualifications:

• Proficiency writing production-quality code in Go and Python (plus PowerShell/Bash) using Git pull requests code review and automated testing.
• Working proficiency building pipelines-as-code (GitHub Actions Azure DevOps GitLab CI Jenkins).
• Experience with infrastructure as code (Terraform Ansible Helm) and GitOps concepts.
• Hands-on experience with at least one major cloud platform (AWS or Azure) and Kubernetes/Docker.
• Familiarity with observability tooling (Grafana Datadog Splunk ELK OpenTelemetry) and basic SRE practices.
• Exposure to test automation policy-as-code and platform security practices.
• Familiarity with ITIL best practices (incident change and problem management) preferred.
• Experience with Lean or Agile methodologies preferred.
• Relevant certifications (e.g. AWS/Azure Associate Certified Kubernetes Administrator) preferred.

Experience:

• 5 years of experience in platform DevOps infrastructure or software engineering roles.
• Bachelors degree in Computer Science Information Technology Engineering or a related field (or equivalent experience).
• Experience supporting business-critical systems ideally within professional services legal or regulated environments.

Gibson Dunn will consider for employment qualified Applicants with Criminal Histories in a manner consistent with the requirements of local law.

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Gibson Dunn is committed to ensuring equal employment opportunities for all qualified applicants including individuals with disabilities. We strive to ensure an inclusive and accessible hiring experience. The Firm will provide reasonable accommodations to qualified individuals with disabilities to enable participation in the application and recruitment process unless doing so would impose an undue hardship in accordance with applicable laws and regulations.

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