Aditya Kulkarni

🚀 Tech Innovator | Machine Learning & AI Engineer 🎓 MS in Computer Science @ UT Dallas | Independent Research project: Visual tracking of aquatic animals using ML-controlled underwater robots 💻 Tech Stack: • Machine Learning & AI: Deep Learning, NLP, Computer Vision, Large Language Models (LLMs), PyTorch, Transformers, LangChain, LlamaIndex, Hugging Face,Vector Databases (Pinecone, FAISS) • Big Data & Analytics: Apache Spark, Kafka, HDFS, SparkSQL, Pandas, Streamlit, Kibana • Cloud & DevOps: Docker, Kubernetes, Jenkins, AWS (S3, EC2, Lambda), Databricks, MLFlow, ELK Stack, Flask, Linux, Git • Databases & Programming: Neo4j, MongoDB, MySQL, Python, C++, SQL 🔍 Current Focus: • Architecting advanced agentic Retrieval-Augmented Generation (RAG) pipelines for transforming complex documents into intelligent, queryable knowledge bases • Designing scalable document processing workflows with OCR-driven visual intelligence and context-aware semantic retrieval 🏆 Key Projects: • Developed an agentic RAG pipeline leveraging AI-powered parsing, dual chunking, and hybrid semantic retrieval for University Lands • Achieved 84% accuracy in software engineering task automation by fine-tuning CodeLlama with advanced PEFT techniques • Improved LLM-based vendor classification and webpage analysis accuracy by 8% at Boehringer Ingelheim • Built real-time news classification and sentiment analysis dashboards processing 50+ articles/min, with 91% sentiment accuracy • Led MLOps implementation for CI/CD and production deployment on OpenShift • Engineered object detection systems for aquatic animals using ResNet50 and YOLO architectures