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# Senior AI Engineer

- **Company:** ARM
- **Location:** Seattle, WA, United States
- **Experience:** Expert
- **Salary:** $170,900.0 - $231,200.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, C++ (Programming Language), Continuous Delivery, Continuous Integration, Linux, DevOps, Elasticsearch, Github, Monitoring of Systems, Python (Programming Language), Machine Learning, Open Source Technology, Ansible, Tensorflow, Prometheus, Secure Coding, Software Engineering, Gerrit, Pytorch, Grafana, Gitlab, Git, Build Management, Gitlab-ci, Kubernetes, Infrastructure Automation Frameworks, Information Technology, Free and Open-Source Software, Machine Learning Operations, Kibana, Terraform, Software Version Control, Docker, Jenkins
- **Published:** July 24, 2026
- **Apply:** https://www.disabledperson.com/jobs/73825853-senior-ai-engineer

## About the Role

* Proficiency with version control systems (e.g. Git), cloud platforms (e.g. AWS, Azure) and Infrastructure as Code tools (e.g. Terraform, Ansible, Packer)
 * Strong background in defining a CI/CD/CT pipeline and building and maintaining a CI/CT/CD infrastructure (e.g., Jenkins, GitLab CI, GitHub Actions)
 * Robust programming and scripting skills preferably in Python / C / C++, previous experience with Linux Systems and containers (e.g. Docker, Kubernetes)
 * Degree or equivalent experience in Computer Science, Electronics, or a related field

"Nice To Have" Skills and Experience:

 * Hands-on experience with CI/CT solutions for Embedded Systems (e.g. SiL / HiL) and monitoring tools (e.g. Prometheus/Grafana/ElasticSearch/Kibana)
 * Knowledge in repository management tools (e.g. Gerrit, GitLab) and CI/CD pipelines for open-source projects
 * Familiarity with ML frameworks (e.g. Pytorch, TensorFlow) and model testing

## Description

As a Senior Machine Learning DevOps Engineer, you will design and build the ML DevOps infrastructure to streamline the deployment of data, models and software in a production-level environment. Be part of a multi-functional team and drive the automation of our development and operations processes thereby ensuring our delivery of AI applications for the Edge is effective and efficient!, * Coordinate the definition of workflows for Edge AI application development and build an end-to-end machine learning pipeline

 * Define secure code practices and implement a repository management strategy for proprietary or open-source software
 * Use continuous integration / continuous deployment and containerization techniques to deploy Machine Learning models to production
 * Collaborate with application and platform developers, data scientists and ML engineers to identify tooling and automation needs and subsequent solutions
 * Identify relevant metrics for DevOps and MLOps practices and use supervision tools to safeguard development efficiency and an operation performance


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