Release Automation Engineer

Overview The Release Automation Engineer owns the engineering of the release pipeline itself - building, hardening, and continuously improving the automated systems that take code from a completed sprint artifact to a verified production deployment. This role owns all of the backend function of releases, including the pipelines, the gates, the promotion logic, the rollback triggers, and the integrations, that make the entire release workflow run with precision and minimal human intervention. This role leverages AI heavily, using it to generate pipeline code, identify automation gaps, analyze failure patterns, and accelerate the engineering of smarter, self-healing release infrastructure. Starting base pay for this role is between $100,000 and $122,000. The actual base pay is dependent upon many factors, such as transferable skills, work experience, business needs, training, location, and market demands. The base pay range is subject to change and may be modified in the future. This role will be eligible for a bonus as well as competitive medical, dental, and vision benefits, wellness reimbursement, life insurance, and a 401(k) with company match. We offer vacation and sick leave benefits (under a flexible time off policy in most states). Responsibilities CI/CD Pipeline Engineering & Ownership • Designs, builds, and owns the CI/CD pipeline infrastructure across the Engineering organization - engineering reliable, fast, and maintainable pipelines in Azure DevOps and GitHub Actions that carry artifacts from code commit through build, test, security scan, and deployment with consistency and precision. • Develops and maintains reusable pipeline templates, shared task libraries, and parameterized pipeline components that allow engineering teams to adopt standardized CI/CD patterns without rebuilding pipeline logic from scratch for every service. • Engineers automated build, test, and packaging stages that produce verified, traceable artifacts, ensuring every artifact that enters the promotion workflow has a complete, auditable build provenance and has passed all required automated quality gates. • Implements and maintains pipeline caching, parallelization, and optimization strategies that minimize build and test cycle times, ensuring pipeline speed never becomes a bottleneck to engineering delivery velocity. • Owns pipeline reliability, monitoring pipeline health, diagnosing and resolving flaky tests and intermittent failures, and driving root cause elimination so that pipeline failures reflect genuine code or configuration problems rather than infrastructure noise. Release Automation & Promotion Logic • Engineers the automated artifact promotion workflow across development, QA, staging, and production environments, implementing promotion logic that enforces defined quality gates, approval requirements, and compliance checks without requiring manual intervention for standard release scenarios. • Builds automated release gates that integrate quality signals from multiple sources, including unit test results, integration test outcomes, SAST/DAST security scan findings, code coverage thresholds, and performance benchmarks, blocking promotion automatically when criteria are not met. • Automates Flyway database migration execution within the promotion pipeline, engineering reliable, sequenced migration runs with pre-migration validation, execution monitoring, and automated rollback triggers that protect environments from failed schema changes. • Integrates LaunchDarkly feature flag operations into the automated release workflow, engineering pipeline steps that provision, configure, and validate feature flag states as part of the promotion process, enabling controlled progressive delivery without manual flag management during deployments. • Builds automated rollback capabilities into the release pipeline, engineering trigger conditions, rollback execution logic, and post-rollback validation steps that enable rapid, reliable recovery from failed deployments without requiring manual intervention under pressure. AI-Augmented Pipeline Engineering • Actively uses AI development tools, including GitHub Copilot, Claude Code and Cursor, to accelerate pipeline engineering work: generating pipeline-as-code, writing automation scripts, building test harnesses, and producing integration logic that connects release tooling across the stack. • Uses AI to analyze pipeline failure patterns at scale, applying AI-assisted log analysis and pattern recognition to identify recurring failure categories, flaky test root causes, and systemic pipeline weaknesses that manual review would take days to surface. • Leverages AI to generate and maintain pipeline documentation, runbooks, and change impact analysis, keeping release infrastructure documentation current with reduced manual overhead as the pipeline evolves. • Evaluates AI-native pipeline intelligence tools, including AI-assisted deployment risk scoring, intelligent ch...