Prajwal Amoghavarsh-AI Engineer

Client: Food Delivery Startup

• Developed machine learning demand forecasting models reducing forecast error (MAPE) by 7% and improving driver allocation and capacity planning.
• Developed and deployed an LSTM based text classification model exposed through Flask REST APIs, improving customer feedback classification accuracy by 13% over baseline models and automating issue routing.

Client: SAHU & ASSOCIATES, Chartered Accountants

• Developed a Document AI pipeline using BERT based Named Entity Recognition (NER) models to extract structured JSON from insurance claims and invoices.
• Developed an internal ML tool to identify anomalous financial patterns across small scale industries, helping Chartered Accountants prioritize audits and investigate high risk companies.

Cross Project Contributions

• Deployed and maintained production ML inference services using Docker and Kubernetes, integrating with automated retraining workflows orchestrated through Airflow.
• Implemented experiment tracking with MLflow to log training metrics and parameters, compare model performance, and support model selection and reproducible ML development.
• Designed lightweight model serving APIs using Flask and Gunicorn (WSGI) reducing inference latency to sub-100 ms while decoupling ML inference from the primary application for independent deployment and scaling.
• Monitored production ML inference services by validating data quality and detecting schema and performance drift.