We are looking for a hands-on AI Engineering Intern who has already built real projects and wants to work on practical AI systems deployed in engineering and infrastructure environments. This is not a research-only position.
You will help develop AI tools that analyze pavement imagery, automate engineering workflows, build AI agents, retrieve knowledge from large document collections, and support next-generation digital products. Projects include computer vision, large language models (LLMs), retrieval-augmented generation (RAG), GIS integration, and edge AI deployment.
Essential Roles and Responsibilities:Build and improve AI agents using modern LLM frameworks
Develop RAG systems that search engineering documents and project archives
Train and evaluate computer vision models for pavement distress detection
Process and analyze drone, GoPro, LiDAR, and roadway imagery
Create image annotation, quality-control, and training pipelines
Experiment with edge AI deployment on NVIDIA Jetson hardware
Build internal applications using Python, Azure AI, Copilot Studio, and APIs
Integrate AI outputs with GIS, dashboards, and engineering workflows
Evaluate commercial AI tools and benchmark them against internal solutions
Prototype ideas quickly and present findings to engineers and leadership
Pursuing BS/MS in:
Computer Science
Artificial Intelligence
Machine Learning
Data Science
Robotics
Computer Engineering
Related field
Strong Python experience
Experience with at least one:
PyTorch
TensorFlow
Ultralytics YOLO
Detectron2
HuggingFace
Experience using LLMs beyond prompting
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