Senior Computer Vision Engineer - Impact & Edge AI

AgileEngine, LLC.

Richmond (VA)

On-site

USD 140,000 - 190,000

Full time

5 days ago

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Benefits offered by this job

Professional growth

Competitive USD-based compensation

Exciting projects

Flextime

Job summary

AgileEngine is seeking a Senior Computer Vision Engineer to own applied model development across CV, ML, and data science use cases. You will build and deploy solutions for detection, segmentation, classification, and video analysis, while considering edge devices and real-world constraints.

The role requires 3–5 years in CV/ML/DS, Python proficiency, and hands-on PyTorch/TensorFlow experience. The position is based in Dallas, TX, onsite, with opportunities for growth, collaboration with Fortune

Qualifications

You must be authorized to work in the United States; visa sponsorship not provided.
3–5 years of professional experience in Computer Vision, ML, DS, or related field.
Degree in Computer Science, Engineering, Data Science, Mathematics, or related discipline (or equivalent practical experience).
Engineers in the US must reside in Dallas, TX, and be onsite.
Strong Python proficiency.
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 ML and data science techniques (time-series forecasting, anomaly detection, regression, clustering).
Experience with computer vision hardware (cameras, sensors, lighting) and deploying on edge devices.

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 architectures, and 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 knowledge of real-world constraints—lighting, sensors, and edge device compute power—to ensure robust production performance.