Lalit Mohan Email & Phone Number

Bengaluru, Ka, In

Bengaluru, Karnataka, India

* 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.

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%

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.

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.

Bachelor Of Technology, Computer Science And Engineering

Special interests in Software Engineering and Machine Learning

Intermediate, Physics, Chemistry, Mathematics, Computer Science, 92%