Skip to main content

Platform / DevOps Engineer Guide

Platform engineers convert AEEF standards into enforceable delivery automation. With AI accelerating code velocity across every team, the CI/CD pipeline is the single most important control surface in your organization. Code that once took days to write now appears in hours, which means your gates must be faster, more reliable, and more comprehensive than ever. If a standard is not enforced in the pipeline, it does not exist in practice. This guide provides the concrete steps to make every quality, security, and compliance standard a hard gate that cannot be bypassed.

What This Guide Covers​

SectionWhat You Will LearnKey Outcome
Pipeline GuardrailsStage design, gate configuration, failure handling, bypass policiesCI stages that enforce quality and security standards automatically
Tooling ProvisioningApproved tool lists, credential management, rollout proceduresControlled rollout of approved AI tools and credentials across teams
Observability for Quality GatesDashboard design, alerting thresholds, drift detection, trend reportingDashboards and alerts for gate failures, pass rates, and compliance drift

Primary Standards​

Prerequisites​

To apply this guide effectively, you should:

  • Have experience managing CI/CD pipelines and infrastructure-as-code for at least one production system
  • Understand the basics of AI code generation and its impact on delivery volume (read the Developer Guide overview for context)
  • Have administrative access to your organization's CI/CD platform, artifact registries, and secret management systems
  • Have authority to enforce pipeline stage requirements and block deployments that fail gates
  • Coordinate with your Development Manager on rollout timelines and with your CTO on infrastructure budget and tooling strategy

Your Expanded Responsibilities​

AI-assisted development expands the platform engineering role in specific ways:

Traditional Responsibilities (Unchanged)​

  • Design and maintain CI/CD pipelines for all services
  • Manage build, test, and deployment infrastructure
  • Enforce environment parity across development, staging, and production
  • Maintain secrets management and credential rotation
  • Ensure uptime and reliability of developer tooling and internal platforms

New Responsibilities (AI-Specific)​

  • Implement mandatory pipeline gates for SAST, SCA, and license compliance on every merge
  • Provision and configure approved AI coding tools (Copilot, Claude, Cursor) with organization-scoped policies
  • Block unapproved AI tools and plugins at the network and endpoint level
  • Instrument pipelines to separately track AI-assisted code metrics (gate failure rates, vulnerability density)
  • Publish gate-failure and compliance dashboards visible to engineering leadership
  • Automate dependency allow-listing and license scanning for AI-suggested packages
  • Coordinate with Security Engineering on scanning rule updates as new AI vulnerability patterns emerge

Key Relationships​

RoleYour InteractionShared Concern
DeveloperProvide fast, reliable pipelines; resolve gate-failure confusion; onboard to approved toolingPipeline speed, clear failure messages, tooling access
Development ManagerReport gate-pass rates and compliance trends; align on rollout schedulesDelivery velocity, quality metrics, rollout risk
CTOInfrastructure budget, tooling strategy, platform roadmapCost efficiency, security posture, architectural standards
Security EngineerIntegrate scanning tools, update rule sets, triage critical findingsVulnerability detection, scanning coverage, incident response
QA LeadAlign test-stage requirements, share gate-failure data, co-own quality dashboardsTest reliability, coverage thresholds, defect trend visibility

Guiding Principles​

  1. If it is not in the pipeline, it is not enforced. Documentation and policy are necessary but insufficient. Every standard must translate into a gate that blocks non-compliant code from reaching production.

  2. Automate enforcement, not just detection. Dashboards that show violations after merge are useful for trends but do not prevent incidents. Prefer hard gates that fail the build over soft warnings that get ignored.

  3. Make gates observable. Every gate must produce structured output -- pass/fail status, failure reason, remediation link. If a developer cannot understand why a build failed within 60 seconds, the gate is poorly designed.

  4. Treat tooling provisioning as a security boundary. AI coding tools have access to source code, internal APIs, and credentials. Provision them with the same rigor you apply to production infrastructure access.

  5. Optimize for developer experience within constraints. Fast pipelines with clear feedback earn compliance. Slow, opaque pipelines encourage workarounds. Invest in caching, parallelism, and actionable error messages.

Use a staged-release pattern with atomic switch for production documentation and static sites:

  1. stage: build and upload a new release directory without touching live traffic.
  2. validate: run smoke checks on staged artifacts.
  3. switch: atomically point current to approved release.
  4. monitor: enforce 15-minute, 1-hour, and 24-hour checks.
  5. rollback: switch to previous or a pinned known-good release when thresholds fail.

This method keeps production stable during build/upload and limits risk to a short switch window.

Implementation references:

Getting Started​

  1. Week 1: Audit your current CI/CD pipelines against Pipeline Guardrails -- identify which AEEF-required gates (build, test, SAST, SCA, license check) are missing or advisory-only
  2. Week 1-2: Enable mandatory gates for the highest-risk gaps; configure them to block merge on failure rather than warn
  3. Week 2-3: Inventory all AI tools in use across teams and standardize provisioning per Tooling Provisioning; revoke unapproved tool access
  4. Week 3-4: Deploy observability dashboards per Observability for Quality Gates and publish the first weekly gate-failure trend report to engineering leadership
info

This guide focuses on the platform and infrastructure perspective. For the developer's approach to working with AI tools, see the Developer Guide. For quality strategy and test coverage requirements, see the QA Lead Guide. For management oversight of delivery risk, see Quality & Risk Oversight.

Next Steps​

  1. Start with Pipeline Guardrails as the primary entry point for this role.
  2. Review the role's key standards in Production Standards and identify your ownership boundaries.
  3. If your team is implementing controls now, use Production Rollout Paths for sequencing and Reference Implementations for apply paths and downloadable repos.
Last updated on by BalrogEg