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Cloud Infrastructure DevOps Engineer; DevOps Engineer Remote
Remote / Online - Candidates ideally in
Herndon, Fairfax County, Virginia, 22070, USA
Listed on 2026-01-02
Herndon, Fairfax County, Virginia, 22070, USA
Listing for:
The Dignify Solutions, LLC
Remote/Work from Home
position Listed on 2026-01-02
Job specializations:
-
IT/Tech
Cloud Computing, SRE/Site Reliability
Job Description & How to Apply Below
SKILLS:
- Cloud Platforms:
Strong hands‑on experience with public cloud platforms (AWS, Azure, GCP), including core services such as virtual networking, compute, storage, IAM, serverless functions, and monitoring. - Terraform:
Deep hands‑on experience developing, maintaining, and troubleshooting infrastructure‑as‑code using Terraform across multi‑cloud environments. - Ansible:
Experience using Ansible to automate day‑to‑day operational tasks, including configuration management and troubleshooting of infrastructure resources. - CI/CD & Git Ops:
Hands‑on experience with Git Lab CI and ArgoCD to design and maintain pipelines for automated testing, deployment, and infrastructure management using Git Ops principles. - Linux & Automation:
Proficient in Linux system administration and automation using Python to streamline infrastructure management and operational tasks. - Serverless Computing:
Experience developing, deploying, and managing serverless functions using cloud‑native services such as AWS Lambda, Azure Functions, and Google Cloud Functions.
- 8 years of experience in cloud infrastructure, Dev Ops, or related engineering roles.
- Deep hands‑on expertise with Terraform, Ansible, Kubernetes, Git Lab CI, and ArgoCD in production environments.
- Strong understanding of cloud platforms (AWS, Azure, GCP) and their core services (compute, storage, networking, IAM, serverless functions).
- Experience developing and maintaining CI/CD pipelines and automating deployments using Git Ops practices.
- Proficient in Linux system administration and infrastructure automation using Python.
- Experience deploying and managing Kafka on Kubernetes, including performance tuning, scaling, and monitoring in a production environment.
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