Senior Software Engineer (Infrastructure)

April 24

Cresta

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Real-Time Intelligence for Contact Centers

AI • Machine Learning • Voice • Chat • Contact Center

51 - 200

Description

• Designer, building, and advancing our core infrastructure that allows the engineering team to execute quickly, productively, and securely. • Join a collaborative but highly autonomous working environment in which each member has a defined role with clear expectations, as well as the freedom to pursue projects they find interesting.

Requirements

• 5+ years experience in DevOps, Site Reliability Engineering, Production Engineering, or equivalent field. • Deep proficiency with coding languages such as Golang or Python. • Deep familiarity with container-related security best practices. • Production experience working with Kubernetes, and a deep understanding of the Kubernetes ecosystem, including popular open-source tooling such as cert-manager or external-dns. Experience with GPU-enabled clusters is a bonus. • Production experience with Kubernetes templating tools such as Helm or Kustomize. • Production experience with IAC tools such as Terraform or CloudFormation. • Production experience working with AWS and services such as IAM, S3, EC2, and EKS. Production experience with database software such as PostgreSQL • Experience with GitOps tooling such as Flux or Argo. • Experience with CI/CD and feature gating systems.Fluency in Linux operations and configurations.

Benefits

• Developer Toolchain. Partner with engineers to build dev tools that empower developer workflows and deployment infrastructure. • Ensure reliability of multi-cloud Kubernetes clusters and pipelines. • Metrics, logging, analytics, and alerting for performance and security across all endpoints and applications. • Infrastructure-as-code deployment tooling and supporting services on multiple cloud providers. • Automate operations and engineering. Focus on automation so we can spend energy where it matters. • Building machine learning infrastructure that enables AI teams to train, test, and deploy on large-scale datasets.