DevSecOps for MLOps

$132,330 - $242,000/Yr

Keysight - Santa Rosa, CA

Part-time,Full-time - Senior

Santa Rosa, CA

10,001+ employees

Computer and Electronic Product Manufacturing

About the position

As a DevSecOps Engineer at Keysight Technologies, you will be instrumental in enhancing the security, reliability, and efficiency of software development processes. This role involves collaborating with cross-functional teams to implement CI/CD pipelines and integrate security practices into the software development lifecycle (SDLC), particularly focusing on machine learning operations (MLOps).

Responsibilities

• Design and implement CI/CD pipelines for software and machine learning projects using tools such as Jenkins, Git, and Artifactory.
• Collaborate with development and data science teams to integrate security and MLOps practices into the SDLC.
• Automate infrastructure provisioning using Ansible and manage configuration with similar tools.
• Monitor and enhance system performance, uptime, and response time, including machine learning model deployments.
• Develop reusable solutions and patterns for continuous integration, delivery, and machine learning workflows.
• Stay informed about industry trends and contribute to the DevSecOps and MLOps community.
• Troubleshoot and resolve issues related to build processes, deployments, and machine learning pipelines.
• Work closely with software architects and data scientists to align DevSecOps and MLOps practices with architectural and model goals.

Requirements

• Master's degree in engineering, computer science, or a related field.
• 5+ years of relevant experience in DevSecOps, MLOps, or related roles.
• Proficiency in programming languages such as CMake, Conan, Python, C++, and Rust.
• Comprehensive technical expertise in various DevSecOps and MLOps toolkits, including Ansible, Cloudbees CI (Jenkins), Artifactory, Jira, Black Duck, Git/Version Control Software, or comparable technologies.
• Familiarity with cloud platforms (AWS, Azure, or on-prem HPC), containerization (Docker, Kubernetes), and machine learning operations tools (Kubeflow, MLflow).
• Experience with version control systems (Git) and build automation tools.
• Passion for continuous learning and staying up-to-date with industry best practices.