Senior Computer Vision Engineer ID72408

AgileEngine•Aurora, CO

1d•Hybrid

About The Position

AgileEngine is an Inc. 5000 company that creates award-winning software for Fortune 500 brands and trailblazing startups across 17+ industries. We rank among the leaders in areas like application development and AI/ML, and our people-first culture has earned us multiple Best Place to Work awards. We are looking for a Senior Computer Vision Engineer to own applied model development across computer vision, machine learning, and data science use cases — building and deploying solutions for object detection, image segmentation, classification, and video analysis. You will evaluate and fine-tune model architectures using PyTorch and TensorFlow, build broader ML models for forecasting and anomaly detection, and apply practical knowledge of computer vision hardware including cameras, sensors, and edge devices.

Requirements

3 to 5 years of professional experience in Computer Vision, Machine Learning, Data Science, or a related field
Degree in Computer Science, Engineering, Data Science, Mathematics, or a related discipline (or equivalent practical experience)
Strong, production-level proficiency in Python
Deep hands-on experience with PyTorch and/or TensorFlow
Proven track record of building and deploying models for detection, segmentation, classification, and image/video analysis
Solid understanding of broader ML and data science techniques (time-series modeling, forecasting, anomaly detection, regression, and clustering)
Practical experience working with computer vision hardware, including cameras, sensors, and lighting setups
Familiarity with deploying models on edge devices
Strong understanding of how physical and real-world constraints impact data quality, model training, and inference
Upper-intermediate English level

Responsibilities

Own the applied model development process across computer vision, AI/ML, and broader data science use cases
Translate complex business problems into viable, practical, and scalable AI/ML solutions
Evaluate various model options, train and fine-tune selected architectures, and rigorously analyze model performance
Develop and deploy solutions for object detection, image segmentation, image classification, and video analysis
Build and maintain models for time-series forecasting, anomaly detection, regression, clustering, and general data analysis
Apply practical knowledge of real-world constraints—such as lighting, sensor limitations, and edge device compute power—to ensure optimal data quality and robust model performance in production