About
Highly accomplished AI/ML Engineer with a robust background in developing and deploying advanced machine learning and natural language processing solutions. Proven expertise in building Retrieval-Augmented Generation (RAG) pipelines, knowledge graphs, and data-driven systems that deliver measurable impact. Adept at leveraging a diverse tech stack including Python, TensorFlow, LangChain, and various databases to solve complex problems and drive innovation. Seeking to apply leadership and technical acumen to challenging AI/ML roles.
Work
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Summary
Driving innovation in legal tech through advanced AI/ML and NLP solutions, focusing on enhancing document understanding, knowledge representation, and automation for legal processes.
Highlights
Engineered an advanced Retrieval-Augmented Generation (RAG) pipeline for legal Q&A, achieving 93% retriever accuracy by leveraging FAISS and adaptive reranking strategies.
Integrated Natural Language Processing (NLP) functionalities via OpenNyai pipeline, including Named Entity Recognition (NER), Rhetoric Roles analysis, and Judgment Summarization.
Implemented a scalable document summarization framework utilizing LangChain MapReduce Chain.
Designed and implemented a Neo4j-based knowledge graph for legal case management, optimizing querying with LangChain GraphCypherQA Chain for structured and full-text semantic search.
Automated court-mandated legal compensation calculations through MACT API integration.
Developed a multilingual document translation application for legal documents, fine-tuning YOLO 11 for layout detection and M2M100 418M for translation.
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Summary
Provided expert-level academic support in computer science, delivering clear and accurate solutions to complex inquiries and aiding student comprehension of core concepts.
Highlights
Provided over 100 timely and precise solutions to complex computer science inquiries on the Chegg platform, demonstrating deep subject matter expertise.
Simplified complex programming, algorithms, and data structure concepts into clear, concise explanations for student comprehension.
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Summary
Contributed to enhancing the online presence and user engagement for a non-profit organization through front-end web development.
Highlights
Contributed to the development and maintenance of the foundation's website, enhancing user experience for over 50 daily unique visitors.
Optimized site compatibility across various devices, leading to a 20% increase in web traffic.
Languages
English
Native
Skills
Programming Languages
Python, SQL, C++.
Data Analysis & Visualization
Pandas, NumPy, Matplotlib, Seaborn, Plotly, Power BI, Microsoft Excel.
Databases
MySQL, PostgreSQL, Neo4j (Graph Database).
Machine Learning
Scikit-Learn, TensorFlow, Keras, Natural Language Processing (NLP), Deep Learning, Computer Vision, Generative AI.
Statistical Analysis
Hypothesis Testing, Regression Analysis, Statistics, Problem Solving.
AI/ML Frameworks & Tools
PyTorch, TensorFlow, Scikit Learn, LangChain, LangGraph, Ollama, Hugging Face, CrewAI, OpenCV, TKinter, VLC.
Web Technologies
HTML, CSS, JavaScript, jQuery.