AI Research Engineer with less than a year in Computer Vision & Deep Learning.
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Computer Vision and Deep Learning engineer with hands-on experience in object detection, segmentation, optical flow, image restoration, and edge-oriented deployment. Strong foundation in Python, C++, PyTorch, OpenCV, and real-world vision pipelines, with project experience spanning multispectral perception, railway safety, OCR, motion analysis, and compression systems.
Indian Institute of Technology, Kharagpur
M.Tech · Vision and Intelligent Systems
August 1, 2024 – June 30, 2026
Maharaj Vijayaram Gajapathi Raj College of Engineering (Autonomous)
B.Tech · Electronics and Communication Engineering
August 1, 2020 – June 30, 2024
Sri Chaitanya Junior College
Intermediate Education · Andhra Pradesh
June 1, 2018 – May 31, 2020
Jawahar Navodaya Vidyalaya
Secondary Education (Xth)
June 1, 2018 – May 31, 2019
AI-Powered Extreme Weather Vision, Obstacle Detection & Pilot Assistance System
January 1, 2025 – June 1, 2026
Developed a real-time multimodal computer vision system for railway and mining environments by integrating RGB cameras, thermal cameras, and radar sensors for robust obstacle detection under fog, dust, rain, glare, and low-visibility conditions. Built a YOLO-based object detection pipeline in PyTorch and OpenCV for pedestrian and obstacle detection under motion blur and long-range operational scenarios. Implemented semantic segmentation and virtual-fence logic for railway-track understanding, intrusion monitoring, and automated alert generation. Designed OpenCV-based camera calibration, homography estimation, and multi-sensor synchronization pipelines to improve localization accuracy and system reliability. Optimized preprocessing, inference, and sensor fusion pipelines for low-latency real-time deployment and field robustness on embedded edge hardware. Deployed optimized inference pipelines on NVIDIA Jetson AGX Orin 64GB using TensorRT acceleration, FP16 optimization, and asynchronous video processing workflows. Profiled runtime latency, GPU utilization, throughput, and memory efficiency across multiple deployment configurations for real-time edge inference.
Smart Retail Loss Prevention and Queue Analytics System
January 1, 2025 – June 1, 2026
Developed a real-time retail video analytics system for customer tracking, queue monitoring, and suspicious activity detection using multi-camera surveillance streams. Built low-latency object detection and multi-object tracking pipelines using YOLO-based detection models and ByteTrack for persistent identity tracking across crowded scenes. Implemented queue-length estimation, wait-time analytics, intrusion detection, and abandoned-object monitoring using temporal motion analysis and region-based event logic. Optimized inference pipelines using ONNX Runtime and TensorRT with Dockerized FastAPI deployment for scalable GPU-backed real-time inference. Integrated RTSP video streaming, asynchronous frame processing, inference logging, and experiment tracking using MLflow and Weights & Biases for production-oriented deployment monitoring.
M.Tech Thesis Deep Image Restoration for Real-World Lens Flare Suppression
August 1, 2024 – March 1, 2026
Designed MFR-Net (Multi-scale Frequency-aware Residual Network) for real-world lens flare removal using FFT-based frequency attention, half-instance normalization, multi-scale residual learning, and physics-guided artifact subtraction. Benchmarked performance against Restormer, Uformer, HiNet, MPRNet, and U-Net on Flare7K++ and FlareReal600. Engineered UltraLightIRGhostUNet specifically for edge deployment using Ghost Convolutions, depthwise-separable decoding, IRTiny blocks, and ECA attention. Applied INT8/FP16 quantization, mixed precision training (AMP), and knowledge distillation to reduce model size and latency while preserving reconstruction quality. Evaluated using PSNR, SSIM, LPIPS, BRISQUE, and NIQE.
OCR and Handwritten Digit Recognition Pipeline
August 1, 2024 – October 1, 2025
Designed a ResNet9-based deep neural network for handwritten Optical character recognition. Curated a custom Telugu numeral dataset of 1,000+ images with diverse handwriting styles, demonstrating end-to-end dataset preparation and curation. Applied transfer learning using MNIST pretraining and fine-tuning on the custom dataset, achieving strong training and validation accuracy.
Optical Flow Estimation and Motion Analysis in Video
January 1, 2024 – January 1, 2025
Estimated dense motion fields using block matching and Lucas-Kanade methods; fine-tuned FlowNetC and FlowNetS on FlyingChairs. Used OpenCV and PyTorch for frame processing, motion estimation, and flow-map generation. Built a custom video dataset with RAFT-generated pseudo ground truth for real-world motion analysis.
Real-Time Object Detection and Classification Pipeline
January 1, 2024 – January 1, 2025
Built an end-to-end object detection and image classification pipeline using YOLO and CNN-based architectures in PyTorch. Prepared annotated datasets using LabelImg and COCO-format workflows with class-balanced augmentation including flipping, color jitter, and mosaic augmentation. Performed preprocessing, cleaning, analysis, training, and validation using OpenCV, NumPy, and Pandas. Evaluated performance using mAP, IoU, precision, recall, and failure-case analysis under occlusion and low-contrast conditions.
JPEG Compression Codec From-Scratch C++ Implementation
January 1, 2024 – January 1, 2025
Implemented a fully functional JPEG encoder/decoder in C++ covering RGB to YCbCr conversion, 8×8 block DCT, quantization, zigzag scan, and Huffman entropy coding. Validated compression fidelity using PSNR and SSIM across multiple quality factors.
Cultural Fit Analysis
The candidate's academic background from a top-tier institution (IIT Kharagpur) and diverse project portfolio demonstrate a strong drive for learning and applying advanced technical concepts. The projects span various domains (image restoration, autonomous systems, retail analytics, OCR), indicating adaptability and a broad interest in AI applications. The focus on optimizing models for edge deployment aligns well with roles requiring practical, deployable AI solutions.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a strong problem-solving aptitude and a systematic approach to engineering complex AI systems. The focus on real-world challenges (e.g., extreme weather vision, lens flare suppression, retail analytics) suggests a practical and results-oriented mindset. The detailed descriptions of optimization and deployment strategies highlight an understanding of operational constraints and performance requirements for AI systems.