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NeelM47/README.md

🚀 AI Engineer | MSc Artificial Intelligence @ UoM

"Engineering intelligent systems that learn, adapt, and deliver real-world impact."

👋 About Me

I'm a passionate Artificial Intelligence Engineer specializing in building intelligent systems that bridge cutting-edge research with real-world applications. With a strong foundation in Machine Learning, Computer Vision, and Natural Language Processing, I thrive on solving complex problems through data-driven solutions.

🔬 Current Focus

  • Advanced RAG Systems: Architecting full-stack, containerized RAG applications using Django, Neo4j, and Google Gemini.
  • Multi-Modal AI: Developing conversational agents capable of reasoning, planning, and autonomous task execution.
  • Production ML: Transitioning academic AI research into robust, scalable enterprise solutions.

💻 Technical Arsenal

🧠 AI & Machine Learning
PyTorch TensorFlow HuggingFace LangChain Google Gemini Scikit-Learn

🛠 Languages
Python C++ Java JavaScript R SQL

💾 Databases & Data Engineering
Neo4j PostgreSQL MySQL Apache Spark Databricks

☁️ Backend, Cloud & DevOps
Django AWS Docker Git Linux

📊 Data Visualization & Tools
Power BI Tableau Jupyter

🎓 Education

MSc Artificial Intelligence | University of Manchester (2023-2025)
Relevant Coursework: Computer Vision, Large Language Models, Cognitive Robotics, Machine Learning Optimization

B.E. Computer Engineering | Bharati Vidyapeeth College of Engineering (2019-2023)
Focus: CNNs, Natural Language Processing, Data Science, Network Security

🏆 Notable Projects

🔍 Knowledge-Extractor RAG System

Full-stack AI application featuring hybrid graph-vector retrieval, cross-encoder re-ranking, and verifiable cited answers from PDF documents.

📊 Enterprise Data Pipeline Architecture

End-to-end AWS Databricks pipelines processing large-scale e-commerce data with Power BI dashboards for business intelligence.

🎯 Symbolic Machine Learning Prover

Intel-partnered project enhancing SMLP for machine learning model analysis and optimization.

📈 What I Bring to the Table

  • Research-to-Production Mindset: Bridging academic AI advancements with practical business applications.
  • Full-Stack AI Development: From algorithm design to deployment and monitoring.
  • Data-Driven Decision Making: Leveraging analytics to drive AI system improvements.
  • Cross-Functional Collaboration: Experience working in Agile teams with stakeholders at all levels.

⚡ Beyond Code

  • 🧠 Continuously exploring the intersection of human cognition and artificial intelligence
  • 📚 Passionate about knowledge sharing and mentoring in the AI community
  • ☕ Fueled by coffee and complex problem-solving challenges

🌐 Let's Connect

Pinned Loading

  1. video-to-book video-to-book Public

    AI-powered pipeline to transform YouTube videos into polished Bionic Reading eBooks. Leveraging Groq (Llama 3.3 & Whisper-Large-v3) for ensemble transcript synthesis.

    Python

  2. mdm-adaptive-inference mdm-adaptive-inference Public

    PyTorch implementation of "Train for the Worst, Plan for the Best." Investigating adaptive token ordering in Masked Diffusion Models (MDMs) to sidestep hard subproblems and elicit reasoning in disc…

  3. Knowledge-Extractor-RAG Knowledge-Extractor-RAG Public

    Production-ready Agentic RAG system featuring an asynchronous Django ingestion pipeline, hybrid Neo4j graph/vector retrieval, cross-encoder re-ranking, and a multi-tool LangChain agent powered by G…

    Python

  4. smlp smlp Public

    Forked from SMLP-Systems/smlp

    Symbolic Machine Learning Prover (SMLP). A framework for the formal verification, safety analysis, and optimization of machine learning models using symbolic logic.

    Python

  5. Road_Damage_Detection Road_Damage_Detection Public

    Deep Learning-based road damage detection and classification. Features a CNN pipeline for image analysis, a web-based monitoring dashboard, and an integrated messaging service for real-time safety …

    Python

  6. summarizer summarizer Public

    Hybrid multi-document summarization system using Sentence-Transformers and KMeans clustering for semantic topic extraction, followed by BART-based abstractive refinement. Fully containerized with F…

    Python