Anirudh Yadav

As a software engineer with three years of experience, I am driven by a deep ambition to achieve mastery in my craft, approaching complex challenges with a logical and concentrated focus. I thrive on autonomous problem-solving, always seeking to build innovative and high-quality solutions that align with a broader, idealistic vision for technological advancement.

Experience

I

Software Engineer

Intello Labs · Jun 2026 – Present

Not yet confirmed

• Developed a camera calibration utility for automated fruit sorting systems using ArUco markers, improving camera alignment and focus while reducing manual calibration effort.
• Developed a Vision-Language Model (VLM) pipeline using Qwen3-VL 32B to automate invoice parsing and information extraction, significantly reducing processing time compared to manual workflows.
• Developed a FastAPI-based application for loading datasets from Annoflow and training RF-DETR models for defect localization across different agricultural commodities.

I

Software Engineer

Indian Council of Medical Research · Apr 2025 – Jun 2026

Not yet confirmed

• Developed and evaluated multiple deep learning models for automated tuberculosis detection using medical imaging data: Custom CNN: Designed a custom architecture achieving 99.57% accuracy.
• Developed and evaluated multiple deep learning models for automated tuberculosis detection using medical imaging data: Modified AlexNet: Optimized the architecture by removing batch normalization and dropout layers, achieving 95.95% accuracy.
• Developed and evaluated multiple deep learning models for automated tuberculosis detection using medical imaging data: VGG16: Applied transfer learning and fine-tuning, achieving 99.05% accuracy.
• Developed a Django-based web portal for real-time TB diagnosis through chest X-ray image upload and automated model inference.
• Developed an end-to-end object localization pipeline using YOLO to detect mosquito breeding habitats from aerial and ground-level waterbody images for AI-assisted vector-borne disease surveillance.
• Trained and optimized deep learning models on a confidential in-house dataset of 13,000+ annotated images, incorporating data preprocessing and augmentation techniques to improve robustness across varying environmental conditions.
• Evaluated model performance using standard object detection metrics including mAP, Precision, Recall and Intersection over Union (IoU), supporting reliable automated identification of potential mosquito habitats.

Software Developer

Jio Platforms Limited · Jul 2022 – Jan 2025

Not yet confirmed

• Led the development of Neural Network models for State of Charge (SoC) and State of Health (SoH) estimation of Lithium-ion batteries for deployment on Edge devices.
• Designed an end-to-end data processing pipeline using battery parameters including voltage, current, 16 internal temperature sensor values and external temperature for battery state estimation.
• Performed data preprocessing, feature engineering and experimentation with multiple Neural Network architectures, including MLP and LSTM models, to identify high-performing approaches for battery state prediction.
• Fine-tuned and evaluated models using experimental and open-source battery datasets, optimizing prediction accuracy and generalization across different operating conditions.
• Deployed and optimized Neural Network models on Edge devices for real-time inference.