Harish Biradar

Innovative Computer Vision and Deep Learning Engineer with a proven track record of developing, optimizing, and deploying AI-powered real-time image and video analytics solutions. Expert in building low-latency inspection pipelines utilizing YOLO architectures, OpenCV, and deep learning frameworks (TensorFlow, PyTorch) to achieve up to 98% detection accuracy. Hands-on experience with hardware-synchronized pipelines, camera configuration, dataset management, and GPU/edge-computing optimizations for mission-critical industrial use cases.

Computer Vision & Deep Learning Engineer - VISTRAS TECHNOLOGIES

(2025-10)

Video Analytics, Industrial Inspection Backend & Vision Pipelines

• Developed a high-availability, real-time video analytics platform using deep learning frameworks, achieving a 98% detection accuracy threshold for mission-critical object identification and industrial classification.
• Engineered a hardware-integrated inference system synchronized with PLC (Programmable Logic Controller) hardware triggers to capture and process images on-demand for automated inspection workflows.
• Optimized the complete end-to-end vision pipeline—from camera frame ingestion via RTSP streams to GPU-accelerated execution—guaranteeing sub-20ms latency thresholds critical for high-speed production.
• Implemented spatial intelligence logic including ROI (Region of Interest) filtering and spatial bounding box matrices to handle precise, zone-specific object counting.

Computer Vision & Deep Learning Engineer - VISTRAS TECHNOLOGIES

(2025-10)

Autonomous Automated Annotation Application ("Auto-Labeler")

• Architected and optimized an automated zero-intervention image and video annotation system leveraging Hugging Face Foundation Models to streamline large-scale dataset preparation.
• Engineered the pipeline to simultaneously process and label 10+ distinct object classes, slashing data collection and validation lifecycle overhead by 96%.
• Trained, fine-tuned, and validated multiple YOLO architectures (v8, v11n, v11s) utilizing a hybrid curation strategy of manual verification and automated preprocessing to establish rigorous ground truth data.
• Elevated core object detection performance from 80% to 93% accuracy through structured feature engineering and iterative hyperparameter tuning.

Computer Vision & Deep Learning Engineer - VISTRAS TECHNOLOGIES

(2025-10)

Thermal Imaging & Environmental Anomaly Detection Suite

• Engineered a real-time analytical monitoring system using computer vision to continuously analyze thermal video streams, map temperature deviations, and isolate critical threshold breaches.
• Programmed custom optics compensation logic, ambient temperature offset configurations, and adaptive environmental calibration loops to guarantee high sensing accuracy across diverse physical spaces.
• Designed and deployed an automated event-triggered alerting pipeline integrated with webhooks to instantly dispatch metadata and anomaly timestamps to remote operational units for proactive risk mitigation.

B.E. - Computer Science & Engineering - ACS College of Engineering, Bengaluru