DevOps Jobs in United States
There are 11,706 open DevOps jobs in United States on freehire right now. 2,790 of them were posted recently. The skills employers ask for most often are cloud, automation and devops.
Salary
| Currency | Period | 25th | Median | 75th | Postings |
|---|---|---|---|---|---|
| USD | year | $133,694 | $162,500 | $192,518 | 1,452 |
| USD | hour | $40 | $55 | $70 | 55 |
From postings that disclose pay. Currencies are counted separately, never converted.
Most requested skills
- cloud 51%
- automation 39%
- devops 32%
- aws 31%
- ci-cd 30%
- python 28%
- kubernetes 26%
- linux 25%
How the work is done
- Remote 1,235 · 11%
- Hybrid 1,123 · 10%
- Onsite 886 · 8%
Visa sponsorship offered in 21% of the 1,847 postings that state a position on it.
Seniority
- Senior 3,221
- Lead 610
- Principal 463
- Staff 329
- Middle 224
- Junior 169
Who is hiring
- 1000+ employees 1,857
- 501-1000 employees 816
- 51-200 employees 323
- 11-50 employees 191
- 201-500 employees 41
- 1-10 employees 19
Systems Administrator I – IT
Supports internal IT operations as an entry-level Systems Administrator, handling tier 1/2 user support, endpoint management, identity administration (Active Directory/Microsoft 365), and compliance documentation in a defense-contracting environment.
Backend AI Document Generation Platform Engineer
The Backend Engineer will design, build, and maintain backend services for an AI-powered document generation platform, managing the full lifecycle of projects from technical design to production.
Senior Backend Platform Engineer - APIs & Auth for Agents
The Senior Backend Platform Engineer will design and maintain public APIs, agent-to-agent protocols, and authentication/authorization systems for the platform. The role focuses on ensuring the reliability and scalability of developer-facing services.
Platform Engineer II
Platform Engineer II managing day-to-day AWS infrastructure (EKS, EC2, RDS, S3, IAM) for a sports tech company, building Terraform modules, Helm charts, and GitLab CI pipelines while driving observability improvements with Datadog.
Senior Software Infrastructure Engineer
Design and implement data infrastructure, authn/authz security architecture, and SOC2 compliance tooling using Python to support AI products and enterprise customers, in-person in NYC.
Senior DevOps Developer
Design, automate, and support Azure cloud-based infrastructure and application environments, driving platform reliability, scalability, security, and deployment efficiency while partnering with development teams.
AWS Cloud Engineer with Kubernetes | Hybrid | W2 Profiles
Hybrid AWS Cloud Platform Engineer role focused on deploying and managing Kubernetes clusters (EKS and AKS), handling networking, service meshes, and troubleshooting in Naperville, IL.
Java with DevOps & SRE
Java developer combining DevOps and SRE duties: build and ship software features, apply SRE principles for reliability and scalability, monitor systems, and implement automation in a 6-month contract based in the San Leandro/San Lorenzo area of California.
Senior DevOps Engineer
Designs and secures CI/CD pipelines, cloud infrastructure, and Kubernetes environments for a restaurant tech company, embedding security into software delivery while enabling rapid, compliant deployments for global POS and ordering systems.
Lead Platform Engineer
Lead a platform engineering team to build and maintain cloud infrastructure, CI/CD pipelines, and observability for a fintech company using AWS, Terraform, and Kubernetes.
Engineering Lead, Platform Engineering
Lead a platform engineering team to build and maintain cloud infrastructure, CI/CD pipelines, and observability systems while driving AI-assisted development practices for a fintech company.
Mid-Level DevOps Engineer
Mid-Level DevOps Engineer designing and supporting secure CI/CD pipelines, cloud infrastructure (AWS, Azure, GCP, OCI), and containerized Java application stacks in classified and unclassified DISA/DoD environments.
Lead Software Engineer - MLOps/Remote
Lead Software Engineer responsible for designing and implementing scalable ML infrastructure, automating ML workflows, and improving model deployment and monitoring in collaboration with Data Science, ML Engineering, and Software Engineering teams.