Senior AI DevOps Engineer for Edge ML Pipelines

Arm Limited; Seattle (WA); On-site; USD 171,000 - 231,000; Full time

Job summary

Arm Limited is seeking a Senior Machine Learning DevOps Engineer to design and build the ML DevOps infrastructure for deploying data, models, and software in production. You will join a multi-functional team to automate development and operations, ensuring efficient delivery of AI applications from Edge to Cloud.

The role emphasizes end-to-end ML pipelines, secure code practices, and containerized deployments, with collaboration across data scientists, ML engineers, and developers.

Qualifications

• Proficiency with version control systems (Git) and cloud platforms (AWS, Azure).
• Strong background in CI/CD/CT pipelines and maintaining CI/CT/CD infrastructure (Jenkins, GitLab CI, GitHub Actions).
• Robust programming in Python, C, C++, Linux systems and containers (Docker, Kubernetes).
• Degree or equivalent in Computer Science, Electronics, or related field.

Responsibilities

• Coordinate the end-to-end ML pipeline workflows for Edge AI applications.
• Define secure code practices and repository management strategies.
• Implement CI/CD/CT and containerization to deploy ML models to production.
• Collaborate with developers, data scientists and ML engineers to identify tooling needs.
• Identify metrics for DevOps/MLOps and use supervision tools to safeguard efficiency.

Skills

Git

AWS

Azure

Terraform

Ansible

Packer

CI/CD pipelines

Python

C

C++

Linux

Docker/Kubernetes

Education

Bachelor's degree in Computer Science or related field

Tools

Jenkins

GitLab CI

GitHub Actions

Docker

Kubernetes

Prometheus

Grafana

ElasticSearch

Kibana

Job description

Arm Limited is seeking a Senior Machine Learning DevOps Engineer to design and build the ML DevOps infrastructure for deploying data, models, and software in production. You will join a multi-functional team to automate development and operations, ensuring efficient delivery of AI applications from Edge to Cloud.

The role emphasizes end-to-end ML pipelines, secure code practices, and containerized deployments, with collaboration across data scientists, ML engineers, and developers.