Senior Computer Vision Engineer - Edge AI & Production ML

AgileEngine; Tampa (FL); Hybrid; USD 140,000 - 190,000; Full time

Benefits offered by this job

Professional growth

Competitive compensation

Exciting projects

Flextime

Job summary

AgileEngine is seeking a Senior Computer Vision Engineer to own applied model development across computer vision, ML, and data science use cases. You will build and deploy solutions for object detection, image segmentation, classification, and video analysis, while evaluating architectures and ensuring robust performance.

The role emphasizes practical knowledge of hardware including cameras, sensors, and edge devices, with emphasis on real-world constraints and scalable AI/ML solutions.

Qualifications

• 3 to 5 years of professional experience in Computer Vision, ML, DS, or related field.
• Degree in Computer Science, Engineering, DS, Math, or related discipline (or equivalent practical experience).
• Experience with PyTorch and/or TensorFlow in production.
• Authorized to work in the US; willing to work onsite in Dallas, TX if required.

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 model options, train and fine-tune architectures, and analyze performance.
• Develop and deploy solutions for object detection, image segmentation, classification, and video analysis.
• Build and maintain models for time-series forecasting, anomaly detection, regression, clustering, and general data analysis.

Skills

Computer Vision

Machine Learning

Data Science

Python

Education

Degree in Computer Science, Engineering, Data Science, Mathematics

Tools

PyTorch

TensorFlow

Job description

AgileEngine is seeking a Senior Computer Vision Engineer to own applied model development across computer vision, ML, and data science use cases. You will build and deploy solutions for object detection, image segmentation, classification, and video analysis, while evaluating architectures and ensuring robust performance.

The role emphasizes practical knowledge of hardware including cameras, sensors, and edge devices, with emphasis on real-world constraints and scalable AI/ML solutions.