AI/Machine Learning Software Engineering Intern

Command Post Technologies, Inc.•Orlando, FL

13h

About The Position

Support the design, development, and deployment of agentic AI systems operating in secure, air-gapped, and edge environments. Work alongside senior engineers to build and test LLM-based pipelines, contribute to agentic workflow development, and assist with model optimization for constrained and offline deployment targets. Gain hands-on experience with real production-oriented AI systems at the intersection of machine learning, systems engineering, and infrastructure-aware deployment.

Requirements

Must currently be pursuing a Bachelor’s degree in Computer Science, Computer Engineering, Software Engineering, or a related technical discipline
Strong Python programming skills
Understanding of basic software engineering principles – code modularity, debugging, and testing
Understanding of machine learning fundamentals and neural network basics
Familiarity with Git and modern software development workflows
Familiarity with REST APIs and basic software integration concepts
Ability to work independently, prioritize tasks, and document work clearly
Effective written and verbal communication skills

Nice To Haves

Experience with LLM inference or serving frameworks such as vLLM, Ollama, llama.cpp, or Hugging Face Transformers
Any hands-on experience with model fine-tuning or distillation, including course projects or personal experiments
Familiarity with agentic frameworks such as LangChain, LangGraph, AutoGen, or similar
Experience deploying or running software in constrained, offline, or non-cloud environments
Exposure to containerization tools such as Docker
Any familiarity with GPU setup or configuration for ML workloads; curiosity about hardware is welcome, deep expertise is not expected
Interest in or exposure to edge hardware platforms such as NVIDIA Jetson, Raspberry Pi, or similar devices

Responsibilities

Contribute to the design and implementation of agentic AI workflows, including multi-agent orchestration, tool use, and reasoning loops
Assist with the deployment of LLM-based systems in air-gapped, on-premises, and edge environments under the guidance of senior engineers
Support the build-out of secure inference pipelines designed to operate without external network access
Write clean, modular code that integrates ML components into broader software systems and pipelines
Run and test models on edge hardware platforms and constrained compute targets; assist with performance and memory optimization
Support model fine-tuning and distillation experiments, including data preparation, training runs, and evaluation
Contribute to reproducible engineering workflows, including version control, containerization, and structured testing
Author and maintain documentation pertaining to deployment processes, system configurations, and experiment results
Troubleshoot issues across the stack, from model behavior through API layer through infrastructure, and report findings clearly
Assist with hardware configuration tasks for GPU workstations and servers as needed, with guidance provided
Engage with senior engineers to understand system changes, contribute to evaluations, and provide feedback for continuous improvement