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DevOps Engineer

A DevOps Engineer automates and streamlines how software is built, tested, deployed, and run. They manage CI/CD pipelines, cloud infrastructure, and monitoring, bridging development and operations to ship faster and more reliably. The role reduces manual toil and downtime, treating infrastructure as code and making deployments routine rather than risky events.

Responsibilities

Build and maintain CI/CD pipelines
Manage cloud infrastructure as code
Set up monitoring, logging, and alerting
Automate deployments and scaling
Improve system reliability and security
Support developers with tooling and environments

Must-have skills

Cloud platform experience (AWS, GCP, or Azure)
Containers and Kubernetes
CI/CD tooling
Infrastructure as code (Terraform, Ansible)
Scripting (Bash, Python) and Linux

Nice-to-have skills

Observability stacks (Prometheus, Grafana)
Security and compliance automation
Service mesh and networking
Cost optimization

Average Salary

Typical US base salary for an HR Generalist by experience level.

Junior

0–2 yrs experience

$95,000 – $120,000
US annual salary (USD)

Intermediate

0–2 yrs experience

$120,000 – $155,000

US annual salary (USD)

Senior

0–2 yrs experience

$155,000 – $205,000

US annual salary (USD)

Figures are annual US market estimates for orientation, not offers. Actual pay varies by location, company stage, and equity.

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Frequently asked questions

What does a DevOps Engineer do?
They automate the build, test, and deployment process and manage the infrastructure that runs applications, aiming for fast, reliable, repeatable releases.
Is DevOps a coding job?
It involves scripting and infrastructure as code rather than heavy application development, but coding and automation skills are central.
What tools do DevOps Engineers use?
Common tools include Docker, Kubernetes, Terraform, and CI/CD systems, along with cloud platforms and monitoring stacks.
How is DevOps different from MLOps?
MLOps applies DevOps principles specifically to machine learning, adding concerns like data versioning, model drift, and retraining.

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