Sachin Shelar

I hold a postgraduate degree in Machine Learning with additional specializations in Deep Learning, NLP, and Agentic AI. Over the past several years, I have led multiple end-to-end AI solution initiatives—from research and prototyping to deployment and scaling. My work includes replacing legacy OCR systems like ABBYY FlexiCapture with advanced transformer-based models such as LayoutLM, Donut, and DETR for document and signature recognition. These projects have significantly improved accuracy, reduced manual intervention, and lowered operational costs. I am a hands-on Python engineer and have built numerous AI-driven applications using frameworks such as Flask, Django, fastAPI and Streamlit, later converting them into robust production services through CI/CD automation, comprehensive testing, and well-structured API layers (Swagger). In my current role, I have also worked with Grafana for observability—creating dashboards for model performance, API latency, and service uptime. Alongside this, I have integrated logging-as-a-service, enabling centralized log collection, alerting, and real-time monitoring across distributed components. These capabilities have helped ensure reliability, traceability, and rapid debugging in production environments.