Software Engineer 1 - Cloud Infrastructure
New
A
Abnormal AICloud infrastructure
Remote - USAFull-TimeEntry
SalaryBase salary range: $122,400 — $176,000 USD
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Job Details
- Experience
- 1-2 years of experience engineering cloud infrastructure solutions for microservice-based systems in production environments; 1+ years of experience with Python; 6 months - 1 year of experience with at least 2 of these areas: Terraform, AWS (or equivalent public cloud provider services), Kubernetes.
- Required Skills
- AWSPythonKubernetesAzureTerraformMicroservices
Requirements
- Bachelor’s degree in Computer Science or a related technical field.
- 1–2 years of experience engineering cloud infrastructure solutions for microservice-based systems in production environments.
- 1+ years of experience with Python.
- 6 months–1 year of experience with at least two of Terraform, AWS or equivalent public cloud provider services, and Kubernetes.
- Experience with source control, testing, and deployment.
- Experience with on-call rotations, incident response, and operating production-grade systems.
- Daily experience using AI coding tools such as Claude Code or Codex, with judgment to validate, simplify, or reject unsuitable AI-generated output.
- Experience with Bash, Golang, or Terragrunt is a nice to have.
- Prior experience building large-scale infrastructure-as-code abstractions is a nice to have.
- AWS certifications are a nice to have.
Responsibilities
- Design, build, test, scale, monitor, and maintain secure, cost-efficient infrastructure in AWS and Azure.
- Develop, evolve, and operate infrastructure as code for repeatable, auditable, and scalable system provisioning.
- Participate in technical discussions and provide constructive reviews.
- Partner with product and application engineering teams to understand platform needs and deliver developer-friendly infrastructure abstractions.
- Participate in an on-call rotation and use customer pain points to identify systemic gaps and improve reliability.
- Address root causes, automate repetitive work, and shape roadmap investments to reduce technical debt and operational toil.
- Use AI to explore, design, and ship solutions and automate repetitive work.
- Improve team and system context through documentation, runbooks, and AI skills.
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