Deep Learning Engineer

An experienced Deep Learning Engineer specializing in Computer Vision, Sensor Fusion, and Multimodal AI to advance R&D; in autonomous aerial systems and geospatial intelligence, working with large-scale datasets for drone flight, sensor data integration, and vision-based autonomy.

Key Responsibilities :

• Design and develop deep learning-based perception systems for drones and autonomous platforms leveraging vision, LiDAR, radar, and IMU sensor data
• Build and optimize multimodal fusion architectures combining image, video, and geospatial sensor data for real-time decision-making
• Implement and fine-tune object detection and segmentation models including RCNN, Fast R-CNN, Mask R-CNN, YOLOv5, SAM, and zero-shot models
• Conduct research and experimentation on large-scale image and video analytics, 3D reconstruction, and scene understanding
• Develop scalable pipelines for model training, hyperparameter tuning, and inference optimization using TensorFlow / PyTorch, TensorBoard, and distributed computing (Ray, Dask, Spark)
• Lead model deployment across GCP-based workflows and design cloud-native AI inference automation systems
• Collaborate with drone engineers to integrate AI models into flight programs and edge devices ensuring low-latency inference and autonomy
• Work with annotation teams using Labelbox, Supervisely, or CVAT to enhance dataset quality and labeling accuracy
• Maintain compliance with geospatial AI data security and regulatory standards ensuring ethical AI deployment and adherence to data-handling protocols

Required Skills :

Preferred (Bonus) Skills :