Platform Engineer - US

Gibson Dunn•Los Angeles, NY

3d•Onsite

About The Position

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 any of our U.S. offices, 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. 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

Requirements

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.
5+ years of experience in platform, DevOps, infrastructure, or software engineering roles.
Bachelor’s 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.

Nice To Haves

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.

Responsibilities

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.