Secure CI/CD Engineer for ML-Driven Deployments

Scale AI; Washington (District of Columbia); On-site; USD 178,400 - 279,000; Full time

Benefits offered by this job

Health benefits

Retirement benefits

Learning stipend

Generous PTO

Commuter stipend

Job summary

Scale AI is seeking DevOps Engineers for the Public Sector to advance our CI/CD pipelines and automate our SDLC. You will work across products to streamline deployments, integrating ML tasks into a cohesive workflow and improving security practices.

You will collaborate with product and engineering teams to enhance code for automated pipelines and contribute to deployment architectures. A fast-paced environment and a drive to learn ML-enabled workflows are essential.

Qualifications

• Experience designing and maintaining CI/CD pipelines.
• Ability to integrate ML components into SDLC workflows.
• Strong collaboration with product and engineering teams to improve deployment automation.

Responsibilities

• Design, develop, and maintain robust CI/CD pipelines for low/high side products.
• Collaborate with teams to improve deployment of existing code within automated pipelines.
• Contribute to architecture of deployment systems for efficiency and reliability.
• Troubleshoot deployment issues to minimize impact on development cycles.
• Understand ML architectures to support integration into pipelines.
• Document pipelines and configurations for maintainability.
• Incorporate security best practices across the SDLC.
• Drive standardization across product teams for unified processes.

Skills

CI/CD

Kubernetes

Terraform

Docker

Python

Bash

PowerShell

Jenkins

GitHub Actions

Azure DevOps

Tools

Kubernetes

Terraform

Docker

Jenkins

GitHub Actions

Azure DevOps

Job description

Scale AI is seeking DevOps Engineers for the Public Sector to advance our CI/CD pipelines and automate our SDLC. You will work across products to streamline deployments, integrating ML tasks into a cohesive workflow and improving security practices.

You will collaborate with product and engineering teams to enhance code for automated pipelines and contribute to deployment architectures. A fast-paced environment and a drive to learn ML-enabled workflows are essential.