AI/Machine Learning Software Engineering Intern at Command Post Technologies, Inc.

Orlando, Florida, United States -

Full Time

Start Date

Immediate

Expiry Date

17 Jun, 26

Salary

0.0

Posted On

19 Mar, 26

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Machine Learning, Software Engineering, LLM, Agentic AI, Deployment, Model Optimization, Inference Pipelines, Git, Containerization, Testing, Fine-Tuning, Distillation, Edge Computing, GPU Configuration, REST APIs

Industry

Defense and Space Manufacturing

Description

Description 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. 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 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 Preferred Qualifications 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

The role involves supporting the design, development, and deployment of agentic AI systems, focusing on building and testing LLM-based pipelines and contributing to agentic workflow development. Responsibilities also include assisting with model optimization for constrained and offline deployment targets in secure environments.