Platform Engineering Intern

Location: Alexandria, VA

Category: Engineering & Technical Services

Employment Type: FULL_TIME

Tags: Corporate, U.S. Citizenship

Overview Systems Planning and Analysis, Inc. (SPA) delivers high-impact, technical solutions to complex national security issues. With over 50 years of business expertise and consistent growth, we are known for continuous innovation for our government customers, in both the US and abroad. Our exceptionally talented team is highly collaborative in spirit and practice, producing Results that Matter. Come work with the best! We offer opportunity, unique challenges, and clear-sighted commitment to the mission. SPA: Objective. Responsive. Trusted. We are seeking a Platform Engineering Intern to support the design, automation, and operation of our internal developer and infrastructure platforms, with growing exposure to AI-enabled workloads and tooling. This role provides hands-on experience at the intersection of platform engineering, cloud infrastructure, and applied AI—focusing on how modern platforms safely and reliably support data-driven and AI-powered systems. You will work alongside senior platform, infrastructure, and security engineers to help build shared services that support application delivery, experimentation, and AI-enabled development. This includes CI/CD pipelines, infrastructure automation, observability, and platform guardrails that enable innovation while maintaining security and compliance. This is a learning-focused role with real responsibility. You will contribute code, automation, and documentation while developing an understanding of how AI workloads change platform design, scaling, and operational considerations. Responsibilities Platform & Automation Assist in building and maintaining platform services that support application and AI-enabled development. Contribute to infrastructure automation using Infrastructure-as-Code tools (e.g., Terraform, ARM, Bicep, or similar). Support CI/CD pipelines, including pipelines used for data processing or AI-related workflows. AI & Data Platform Exposure Gain hands-on exposure to how platforms support AI and data workloads (e.g., model training, inference services, data pipelines). Assist with environment setup for AI experimentation, including compute, storage, and access controls. Learn how AI workloads impact scaling, cost management, security, and observability. Cloud, Containers & Systems Work with cloud and virtualized environments supporting compute, storage, and networking. Assist with containerized workloads and Kubernetes-based platforms where applicable. Help maintain baseline configurations and platform standards. Security, Reliability & Governance Learn and apply security-by-design principles, including least privilege and environment isolation. Assist in monitoring platform health and basic observability for both application and AI-related services. Support documentation, configuration validation, and operational readiness efforts. Collaboration & Learning Collaborate with platform engineers, application teams, and security stakeholders. Participate in technical discussions, sprint planning, and design reviews. Document work clearly to support knowledge sharing and repeatability. Qualifications Required Qualifications: Candidates must be actively enrolled in an accredited, degree-seeking undergraduate or graduate program and have at least one semester remaining following the internship Currently pursuing a degree in Computer Science, Engineering, Data Science, Information Systems, or a related field. Foundational understanding of at least one programming or scripting language (e.g., Python, PowerShell, Bash, or Go). Basic familiarity with Linux or Windows systems. Introductory understanding of AI or machine learning concepts (e.g., models, training vs. inference, data pipelines). Intense curiosity about how cloud platforms, automation, and AI systems work together. Ability to learn quickly, follow technical guidance, and complete assigned tasks.

Responsibilities

Platform & Automation Assist in building and maintaining platform services that support application and AI-enabled development. Contribute to infrastructure automation using Infrastructure-as-Code tools (e.g., Terraform, ARM, Bicep, or similar). Support CI/CD pipelines, including pipelines used for data processing or AI-related workflows. AI & Data Platform Exposure Gain hands-on exposure to how platforms support AI and data workloads (e.g., model training, inference services, data pipelines). Assist with environment setup for AI experimentation, including compute, storage, and access controls. Learn how AI workloads impact scaling, cost management, security, and observability. Cloud, Containers & Systems Work with cloud and virtualized environments supporting compute, storage, and networking. Assist with containerized workloads and Kubernetes-based platforms where applicable. Help maintain baseline configurations and platform standards. Security, Reliability & Governance Learn and apply security-by-design principles, including least privilege and environment isolation. Assist in monitoring platform health and basic observability for both application and AI-related services. Support documentation, configuration validation, and operational readiness efforts. Collaboration & Learning Collaborate with platform engineers, application teams, and security stakeholders. Participate in technical discussions, sprint planning, and design reviews. Document work clearly to support knowledge sharing and repeatability.

Qualifications

Required Qualifications: Candidates must be actively enrolled in an accredited, degree-seeking undergraduate or graduate program and have at least one semester remaining following the internship Currently pursuing a degree in Computer Science, Engineering, Data Science, Information Systems, or a related field. Foundational understanding of at least one programming or scripting language (e.g., Python, PowerShell, Bash, or Go). Basic familiarity with Linux or Windows systems. Introductory understanding of AI or machine learning concepts (e.g., models, training vs. inference, data pipelines). Intense curiosity about how cloud platforms, automation, and AI systems work together. Ability to learn quickly, follow technical guidance, and complete assigned tasks.