Senior Engineer, DevOps (US)

Role overview

What you'll do

• Design and maintain scalable cloud infrastructure across AWS, Google Cloud Platform, and Microsoft Azure with a focus on high availability.
• Build and evolve CI/CD pipelines for fast and safe software delivery from commit to production.
• Embed DevSecOps through security scanning, secrets management, and compliance by default.
• Lead infrastructure-as-code practices with Terraform, CloudFormation, and Ansible.
• Own monitoring, alerting, logging, and tracing across systems.
• Drive cloud cost optimization and proactively right-size resources.
• Improve developer experience and remove friction from the software development lifecycle.
• Maintain architecture documentation, runbooks, and post-mortems.

What you'll need

• Extensive experience provisioning and managing cloud infrastructure on AWS, Google Cloud Platform, and/or Microsoft Azure.
• Strong proficiency with Terraform, AWS CloudFormation, and Ansible.
• Experience with CI/CD pipelines in GitHub Actions, Bitbucket Pipelines, or Jenkins.
• Deep expertise with Docker and Kubernetes.
• Experience designing high-availability and fault-tolerant architectures.
• Experience with blue-green deployments, canary releases, and feature flags.
• Experience with serverless architectures using AWS Lambda, Google Cloud Functions, or Azure Functions.
• Experience with edge platforms, CDNs, developer platforms, security tools, and observability platforms.
• Solid Linux administration, NGINX, Traefik, load balancer, networking, and web application experience.
• Proficiency with Bash and Python for automation and internal tooling.
• Experience with Git, managed code platforms, and cloud cost optimization or FinOps practices.
• Experience using and evaluating AI-enabled development tools and agent-driven workflows.

AI-enabled engineering

• Comfort leveraging AI-enabled tools and workflows for engineering, automation, debugging, and operational tasks.
• Experience selecting models, tools, and execution patterns for multi-step AI or agent-driven workflows.
• Ability to review and harden AI-assisted output for security, scalability, maintainability, and architectural fit.
• Experience building prompts, evaluation frameworks, documentation, or operational context systems that improve engineering reliability.
• Familiarity with automated evaluation and feedback loops for AI-enabled systems.

Nice to have

• Experience in an agency, consultancy, or enterprise SaaS organization.
• AWS, Google Cloud Platform, or Microsoft Azure cloud certification.

About Code and Theory

Compensation

• The compensation range is $110,000 to $150,000 yearly and spans multiple levels.
• Actual compensation depends on skills, experience, budget, and location.