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Engineering Manager, Platform Engineering

Job in New York City, Richmond County, New York, USA
Listing for: Gibson Dunn
Full Time position
Listed on 2026-08-24
Job specializations:
  • Software Development
    DevOps, Software Project Mgr/ Lead, Cloud Engineer - Software
Job Description & How to Apply Below

Engineering Manager, Platform Engineering

Based in New York City or Washington, D.C., the Engineering Manager, Platform Engineering leads and grows the team responsible for the firm's internal developer platform: the reusable platforms, tools, and services that improve developer productivity, accelerate delivery, and standardize engineering practices across the firm. This role is accountable for the team's people, delivery, reliability, and security outcomes, balancing hands-on technical leadership with the management of engineers.

The Engineering Manager owns the team's roadmap and execution, develops engineers, sets the operating rhythm for delivery, release management, and on-call, and partners with engineering and business leaders to ensure the platform meets the firm's needs.

Primary applications and platforms include:

  • 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

Responsibilities include:

Team Leadership & People Management

  • Lead, coach, and develop a team of platform engineers, fostering a high-performing, inclusive, and collaborative engineering culture.
  • Own performance management, career development, goal-setting, and retention; provide regular, actionable feedback.
  • Balance individual growth with team delivery, removing blockers and supporting engineers' success.

Delivery & Roadmap Ownership

  • Own the Platform Engineering roadmap and delivery, translating firm and engineering strategy into prioritized, well-scoped engineering work.
  • Ensure predictable, high-quality delivery through effective planning, execution, and risk management.
  • Establish and run the team's operating rhythm (planning, standups, code review culture, retrospectives, and on-call).

Technical Leadership & Engineering Standards

  • Review designs and code, guide architecture, and make sound technical trade-offs.
  • Shape platform architecture, coding standards, and long-term direction.
  • Champion a code-first, everything-as-code culture and measurable developer-experience outcomes.
  • Ensure the team extends platform standards to AI systems, including version-controlled prompts, retrieval configurations, evaluations, and release processes alongside application code.

Reliability, Security & Operational Excellence

  • Hold accountability for platform reliability, SLOs, incident management, and on-call health, and continuous improvement of operational maturity.
  • Ensure robust pipelines-as-code, release-management, and test-automation practices that enable safe, frequent delivery.
  • Ensure platform security and compliance obligations are met, partnering with security and governance teams.
  • Ensure the platform accounts for AI-specific operational and security realities, including provider outages, graceful degradation across models, non-deterministic failure modes, silent quality regressions after model updates, inference and GPU cost management, prompt-injection and data-exfiltration defenses, output filtering, and confidentiality constraints across prompts, retrieval, and agent tool access.

Stakeholder & Vendor Management

  • Partner with development teams, business stakeholders, and IT leadership to align platform investments with firm needs.
  • Manage relationships with strategic vendors, along with…
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