Sunit Adhikary

LeadingtheNationalJobsPortalforWadhwaniFoundation,architectinganagenticAIplatformwithRAGpipelinesandmulti-agent
workflows for job discovery, skill-matching, and career guidance.

Bootstrapped an AI-assisted wedding planning and venue-booking platform, grew FY24–25 revenue to ₹1.8Cr, served 300+ clients, and scaled operations with a 7-member core team.

Architected Jio HES+ Copilot and DISCOM intelligence suite using LLMs, RAG, and agentic execution to consolidate 20 dashboards into a single Copilot, achieving sub-2.5s p95 latency and 300% officer engagement increase.

Developed 3D vision systems for segmentation, detection, 6D pose estimation and motion capture, and deployed scalable deep-learning services on Google Cloud and edge devices.

Built computer-vision algorithms for agricultural product quality assessment and automated fine-leaf counting, deployed on NVIDIA Jetson to improve fair compensation for tea cultivators.

Implemented NLP pipelines for sentiment analysis, named-entity extraction for Scandinavian languages, text classification, and a deep-learning based text summarization module.

Developed color detection and faster object localization techniques, implemented OCR for number-plate detection using CTPN+CRNN and logo detection with YOLO.

Benchmarked ML frameworks (MXNet, scikit-learn, Keras, TensorFlow) for training time and memory usage and evaluated optimization techniques across datasets with varying sparsity.

Applied deep-learning techniques to clean and extract tortuous letters from stone inscription images, improving character extraction robustness.

Worked on FTIR image classification for hyperplasia detection using NMF band reduction and Random Forests, producing cell maps indicative of hyperplasia.

Developed semi-supervised techniques for 3D urban LiDAR point-cloud classification, feature extraction, and visualization tooling using Random Forests and CRF frameworks.

Completed a Postgraduate Program in Product Management with emphasis on data science product management from Sep 2022 to Jul 2023.

Grade: 7.71/10

Completed a Bachelor of Technology in Computer Science and Engineering from Aug 2014 to May 2018 with a GPA of 7.71/10.

Grade: 89.6%

Completed Class XII (AISSE/ISC equivalent) with an aggregate percentage of 89.6% and 94.3% in PCM.

Grade: 10/10

Completed Class X (AISSE) in Jan 2002–May 2011 with a CGPA of 10/10.