Machine Learning Infrastructure Engineer (Associate or Experienced)

Boeing•Huntsville, AL

1d•Onsite

About The Position

The Boeing Company is seeking an Infrastructure Engineer (Entry Level or Associate) in Huntsville, AL. This Infrastructure Engineer will support our Artificial Intelligence Innovation Lab infrastructure environments with an emphasis on Artificial Intelligence and Machine Learning (AI/ML) development platforms. This position supports Linux and Windows systems, cloud and virtualization platforms, container orchestration technologies, and secure networking services. The selected candidate will work under general direction and collaborate with cross-functional teams including AI/ML engineers, DevOps, cybersecurity, and network engineering.

Requirements

Bachelor’s degree
Ability to obtain a U.S. Security Clearance for which the U.S. Government requires U.S. Citizenship
1+ years of experience with LINUX system administration
1+ years of experience developing software using Docker or Kubernetes for container-based applications
1+ years of experience with computing networking/storage concepts and architecture

Nice To Haves

Bachelor of Science degree from an accredited course of study in engineering, engineering technology (including manufacturing engineering technology), chemistry, physics, mathematics, data science, or computer science and 2+ years of related work experience OR Bachelor’s Degree and 6+ years of directly related work experience OR 10+ years of related, relevant experience
Ability to troubleshoot technical issues and communicate effectively with cross-functional teams
Experience with cloud platforms such as AWS, Azure, or Google Cloud
Experience supporting lab infrastructure environments
Experience or exposure to virtualization technologies such as VMware, Hyper-V, Proxmox, or similar
Basic experience with automation or scripting tools (Python, Bash, PowerShell, Ansible, Terraform, or similar)
Familiarity with CI/CD concepts and tools, preferably GitLab
Understanding of GPU compute concepts or high-performance workloads
Experience working with Kubernetes clusters in production or lab environments
Familiarity with distributed storage systems and storage networking
Knowledge of security best practices and compliance frameworks such as NIST
Experience supporting AI/ML or high-performance computing (HPC) environments
Experience troubleshooting VPN and complex network connectivity issues
Active U.S. Secret Security Clearance

Responsibilities

Supports Linux and Windows system administration tasks including system monitoring, patching, updates, and routine maintenance
Supports compliance with enterprise IT policies, cybersecurity standards, and regulatory requirements
Assists with deployment and support of ML Ops tooling used to manage GPU computing resources for AI/ML workloads
Supports management and operation of computing infrastructure used by AI and ML development teams
Assists in configuring and maintaining network devices (firewalls, switches) to ensure secure and reliable operations
Helps troubleshoot network connectivity issues, including VPN access, escalating complex issues to senior engineers as required
Assists in optimizing cloud infrastructure resources to improve performance, cost efficiency, and scalability
Supports virtualization platforms and cluster technologies, ensuring availability and performance
Assists with administration of distributed storage and storage networking systems
Supports Kubernetes cluster operations using platforms such as Rancher and OpenShift, ensuring cluster health and security
Uses Kubernetes tooling to assist with containerized application deployment and orchestration
Assists in administration of core network services, including DNS, DHCP, and LDAP across Linux and Windows environments
Helps document infrastructure changes and asset data in Data Center Infrastructure Management (DCIM) and IP Address Management (IPAM) systems
Assists with deployment and management of local generative AI models, supporting infrastructure optimization for AI workloads
Collaborates with DevOps teams to develop, support, and troubleshoot CI/CD pipelines, primarily using GitLab
Assists with automation of infrastructure provisioning and deployment workflows using scripting and infrastructure-as-code tools