Mar 2022 – Jul 2024 · 2 yrs 4 mos · Bengaluru, Karnataka, India · Hybrid

• Developed Image Forgery Detection (IFD) and PDF Forgery Detection products for major clients like BofA, MetaMap, AXA, and Uber.
• Engineered advanced document skew correction (Page Dewarp) and integrated PaddleOCR, boosting OCR accuracy by 10% and 25% respectively for various financial documents.
• Implemented Image Quality Assessment (IQA) for enhancing real-world documents and improving OCR accuracy on low-quality images.
• Designed and maintained deep learning models for Image Forgery Detection, Automatic Signature Cleaning, and Verification.
• Managed the complete ML lifecycle for multiple projects, including data collection, generation, exploration, and training.
• Text enrichment which increased LLM extraction accuracy by 15%.
• Spearheaded the development of Tabular Framework, an LLM-powered product for detecting and extracting tabular data from various document types by accepting natural language prompts.
• Created benchmark datasets and scoring modules to ensure continuous product improvement and early regression detection.
• Contributed to building RAG pipelines for long documents and Agentic workflows for complex, multi-step tasks.
• Fine-tuned open-source SLMs using LoRA and constructed an Enterprise DB Search component using Weaviate Vector DB and LLMs.
• Led hackathon teams, resulting in new product features, and participated in hiring processes, including giving pre-placement talks and mentoring interns on Image Forgery Detection and Semantic Document Comparison projects.

May 2007 – Oct 2020 · 13 yrs 5 mos · Bengaluru, Karnataka, India

• Individual contributor (95%)
• Mentoring a team of 12-15 engineers (5%)
• C++ Product Development
• Utilized ML algorithms like SVM and Gradient Boosted Machines to achieve Cross Validated performance of more than 95%, using domain specific features
• Achieved 98% character classification score using Fully Connected Neural Network architecture of Deep Learning without handcrafted features.
• Created Convolutional Neural Network (CNN) model for offline handwriting recognition which improved results by 20%

Jul 2005 – Apr 2007 · 1 yr 9 mos · Bengaluru, Karnataka, India

• Analyzed large data sets and developed statistical and classification models using Kernel Density Estimation, Logistic Regression and Random Forests which improved accuracy by 6%
• Built Ensemble Models using Stratified Sampling which beat individual models built without stratification
• Designed and implemented Handwritten Note-Taking Apps for Android, iOS and Windows as a front-end to the Recognition Engine and for data gathering.

Jul 2003 – Jun 2005 · 1 yr 11 mos · Bengaluru, Karnataka, India

• Implemented various Proof of Concepts in Matlab and C++
• Feature Engineering using signal processing, curve matching and segmentation of data enabled performance to increase by 10%
• Performed Feature Importance and Selection tests which resulted in improved models and faster training
• Participated in all phases of data exploration: data aggregation, cleaning, validation, visualization, partitioning, augmentation and imbalance reduction
• Streamlined Data Collection and Preparation phase.