Senior DevOps Engineer (Azure, CI/CD, AI Agents)

About the client:

An established US healthcare staffing company that helps medical facilities find the clinicians they need so patients get care on time.

The DevOps team is building platform reliability for a growing engineering organization: moving CI/CD to CircleCI and GitHub, automating Azure infrastructure with Terraform, strengthening observability and security, and putting AI agents to work across delivery. The team works automation-first, runs blameless reviews, and uses DORA metrics to decide where to invest.

Key Responsibilities

• Help drive the move to CircleCI and GitHub; design reusable pipeline patterns, orbs, and fast feedback loops (caching, parallelism, test selection) for 50+ engineers.
• Design Terraform modules and self-service paths on Azure and AKS that teams adopt without filing a ticket.
• Build and operate Claude-based agents and MCP servers that automate triage, pipeline diagnostics, and release tasks, backed by evals, tracing, and guardrails.
• Measure build time, lead time and deployment frequency; find friction by working with engineers and remove it at the root.
• Embed SAST/DAST, secret scanning and dependency analysis; use LaunchDarkly, progressive delivery and automated rollback, so teams move fast with confidence.
• Set Datadog and Sentry.io standards for SLOs, alerting, and release health that cut time to detect and resolve.
• Build CLIs and services other engineers build on, treating contracts, versioning, and ergonomics as product concerns.
• Mentor DevOps I and II engineers through pairing and code review.

Required Qualifications

• 8+ years in DevOps, Site Reliability Engineering, or Platform Engineering.
• Deep CI/CD experience (CircleCI preferred; GitHub Actions, Jenkins, or similar).
• Production Python development (CLIs, APIs, automation, testing).
• Terraform proficiency: modules, state management, and testing.
• Azure experience across AKS, networking, identity, and compute.
• Kubernetes in production (Helm, scaling, observability).
• GitHub expertise: workflows, API and security configuration.
• Hands-on work with LLMs and agents (tool calling, MCP) and a practical view of where AI helps and where it does not.
• Ability to drive cross-team work and earn adoption without formal authority.