AI/ML Engineer with Drone Imagery

Bright Vision Technologies•Houston, TX

1d•Remote

About The Position

Bright Vision Technologies is a forward-thinking software development company dedicated to building innovative solutions that help businesses automate and optimize their operations. We leverage cutting-edge technologies to create scalable, secure, and user-friendly applications. As we continue to grow, we’re looking for a AI/ML Engineer with Drone Imagery to join our dynamic team and contribute to our mission of transforming business processes through technology. This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential. Bright Vision Technologies is seeking an experienced AI/ML Engineer with Drone Imagery experience for a long-term hybrid contract opportunity. The ideal candidate will have strong hands-on experience in machine learning, computer vision, drone imagery, aerial image analytics, anomaly detection, object detection, image segmentation, and scalable image data processing. This role will focus on accelerating automation across drone programs by extracting and structuring data from drone models, applying AI/ML models to detect anomalies from drone imagery, building scalable drone photo storage and management solutions, and supporting autonomous or semi-autonomous drone operations. The ideal candidate should have experience working with drone imagery or similar visual datasets involving towers, buildings, vertical structures, ground placement environments, telecom assets, infrastructure inspection, construction sites, utilities, or industrial assets.

Requirements

8+ years of overall IT / data / AI / software engineering experience.
Strong hands-on experience in machine learning, deep learning, computer vision, and image analytics.
Practical experience working with drone imagery, aerial imagery, satellite imagery, remote sensing, LiDAR, photogrammetry, 3D models, or large-scale image datasets.
Strong programming experience with Python.
Hands-on experience with AI/ML frameworks such as PyTorch, TensorFlow, Keras, OpenCV, Scikit-learn, YOLO, Detectron, Mask R-CNN, or similar tools.
Experience building object detection, image segmentation, classification, anomaly detection, and visual inspection models.
Experience processing large image datasets, including image cleaning, labeling, augmentation, feature extraction, and metadata management.
Experience with scalable data storage and processing for drone photos, image datasets, geospatial data, or inspection records.
Strong understanding of model training, validation, testing, deployment, and monitoring.
Experience with MLOps tools and practices such as MLflow, Docker, Kubernetes, CI/CD, model registry, model monitoring, or automated retraining.
Experience with cloud platforms such as AWS, Azure, or GCP.
Ability to work with cross-functional teams including field operations, engineering, inspection teams, data engineers, and business stakeholders.
Strong analytical, problem-solving, and communication skills.

Nice To Haves

Experience with tower inspections, telecom infrastructure, utility inspections, construction site inspections, energy assets, industrial inspection, buildings, vertical structures, or ground placement environments.
Experience with drone platforms, drone inspection workflows, drone photo management, or drone program automation.
Experience with GIS, geospatial data, spatial indexing, mapping systems, or location-based analytics.
Experience with LiDAR point clouds, 3D reconstruction, photogrammetry tools, or digital twin environments.
Experience with Databricks, Snowflake, Spark, or distributed data processing platforms.
Experience deploying AI/ML models into enterprise production environments.
Experience building APIs or services to expose ML model outputs to business applications.
Experience with annotation platforms, data labeling workflows, active learning, or synthetic data generation.
Prior experience in telecom, wireless infrastructure, construction, energy, utilities, engineering, or industrial asset inspection domains.

Responsibilities

Design, develop, and deploy AI/ML models for drone imagery analysis, aerial image processing, and automated visual inspection use cases.
Extract, clean, classify, and structure data from drone models, aerial images, 3D scans, photogrammetry outputs, and image-based inspection datasets.
Build computer vision models to detect anomalies, defects, damages, missing components, structural issues, equipment placement issues, and visual inconsistencies from drone imagery.
Develop object detection, image segmentation, classification, and pattern recognition models using frameworks such as PyTorch, TensorFlow, OpenCV, YOLO, Detectron, or similar tools.
Work with drone photo datasets, image metadata, geospatial references, 3D models, LiDAR outputs, and photogrammetry-based inspection data.
Design scalable data pipelines for storing, processing, indexing, and retrieving large volumes of drone photos and image-based inspection records.
Support automation across drone programs by reducing manual review effort and improving accuracy in image-based inspection workflows.
Collaborate with business, engineering, field operations, data science, and platform teams to understand inspection requirements and convert them into AI/ML solutions.
Develop models and pipelines to support tower, building, vertical structure, ground equipment, utility, infrastructure, or industrial site inspection use cases.
Build repeatable model training, validation, testing, deployment, and monitoring workflows for drone imagery analytics.
Perform image preprocessing, annotation review, feature extraction, augmentation, model tuning, and performance evaluation.
Work with MLOps practices to support model versioning, experiment tracking, CI/CD, monitoring, retraining, and production deployment.
Design solutions for drone image storage, photo cataloging, metadata tagging, retrieval, and lifecycle management.
Support autonomous or semi-autonomous drone operations by contributing to AI-assisted detection, classification, and decision-support workflows.
Evaluate model accuracy, false positives, false negatives, precision, recall, and other performance metrics for computer vision models.
Collaborate with data engineering teams to integrate drone imagery outputs with enterprise systems, dashboards, reporting platforms, and workflow tools.
Troubleshoot model performance issues and optimize computer vision pipelines for scalability, speed, accuracy, and cost efficiency.
Document model logic, data flow, assumptions, limitations, and operational procedures for business and technical stakeholders.
Ensure AI/ML solutions follow data governance, security, compliance, and responsible AI practices.