PJAbout Pranamya
Turnkey AI engineering that delivers measurable business impact. I design, build, and deploy production-grade ML systems—data pipelines, feature engineering, custom models, MLOps, and cloud deployments—and embed them into product workflows: NLP/chatbots, recommendation engines, computer vision, and automation. Outcome focus: increase revenue, cut operating costs, and speed decisions. Founder, Jin Labs.
English
Native or bilingual
Remote only
Primarily works remotely
Experience
- Jin LabsCo-Founder and CTOJuly 2025 - Today (11 months)Bengaluru, Karnataka, IndiaAt Jin Labs, I architect and scale the core technologies that power our intelligent AI solutions transforming ideas into products that businesses can rely on daily.
- Anuyog Software SolutionsIntern - Machine Learning EngineerMarch 2025 - May 2025 (2 months)• Conducted Exploratory Data Analysis (EDA) on the Leafsnap dataset to understand distribution, identify duplicates, and prepare clean inputs.• Implemented and compared SVM, KNN, and Naive Bayes classifiers, achieving up to 98% validation accuracy with SVM.• Designed and trained a Convolutional Neural Network (CNN) from scratch for image classification, incorporating preprocessing, normalization, and augmentation.• Experimented with EfficientNetB0 transfer learning, applying label smoothing, class weighting, dropout, and cosine learning rate scheduling to push accuracy to 86.7% on the test set.• Built an interactive Flask application with image upload and webcam integration, enabling real-time plant species recognition through the trained CNN model.• Enhanced workflows with reproducibility, error-handling, and proper dataset structuring for scalable experimentation.This internship strengthened my expertise in image processing, model training, debugging, and deployment, bridging the gap between research and practical AI applications.
- Infosys SpringboardProject InternNovember 2024 - January 2025 (2 months)• Designed and implemented end-to-end image processing pipelines — from preprocessing (resizing, normalization, segmentation) to feature extraction using texture (GLCM), shape (contour analysis), and color histograms.• Built and trained a Random Forest Classifier on extracted features, enabling accurate and reliable disease detection.• Developed visualization workflows to provide clinicians with interpretable insights into feature distributions and image processing steps.• Leveraged Python libraries such as OpenCV, scikit-image, scikit-learn, and Matplotlib to ensure a robust and scalable implementation.• Delivered a reproducible pipeline with model persistence using Joblib, making the solution practical for real-world medical applications.
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Education
- Artificial Intelligence Primer Certification Principles of Generative AI Certification Prompt Design in Vertex AI Skill BadgeArtificial Intelligence Primer Certification Principles of Generative AI Certification Prompt Design in Vertex AI Skill Badge
- Bachelor of EngineeringRamaiah Institute Of Technology2026Bachelor of Engineering