Aakash Kumar

Architected and deployed an end-to-end Cash Flow Forecasting and Credit Assessment System for a global investment bank, integrating multiple data sources and ML pipelines with MLOps best practices. Developed and deployed a Qwen vision language model for automated invoice extraction on-premises, implementing a multimodal AI system that processes financial documents with high accuracy.

Developed machine learning solutions using PyTorch for diverse industry applications, demonstrating expertise in communicating technical concepts to both technical and non-technical stakeholders. Built production-grade ML workloads on cloud platforms, applying data science best practices and optimization methodologies to deliver business value.

Implemented machine learning pipelines for customer data analysis using pandas and scikit-learn, developing solutions that enhanced customer engagement and business value through data-driven insights. Contributed to ML system architecture leveraging computer vision and NLP techniques to solve industry-specific problems.

Grade: 3.47/4 CGPA

Activities and societies: Relevant Coursework: Computer Science, Statistics, Engineering, Machine Learning, Deep Learning, Natural Language Processing, and Operations Research

Studied Data Science, covering core concepts in Computer Science, Statistics, and Engineering. Focused on Machine Learning, Deep Learning, Natural Language Processing, and Operations Research.