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Achievement: YOLOAchievement: Pull Shark

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RudraxDave/README.md

Hi, I'm **Rudrax Dave**

**AI/ML Engineer | Data Scientist | Software Engineer**

A highly experienced **AI/ML Engineer, Data Scientist, and Software Developer** with over 4+ years of expertise in delivering impactful AI/ML models and deploying them into scalable, production-ready environments [1-6]. Holds a **Master of Science in Electrical Engineering and Computer Science (Machine Learning & Data Science) from the University of Southern California (USC)** [3, 7-14].

Specializing in **Generative AI, Large Language Models (LLMs), Computer Vision, and predictive modeling** [2-6]. Proficient in AI frameworks such as **PyTorch and TensorFlow**, advanced data management techniques, and developing and optimizing scalable machine learning algorithms for real-world industrial settings, particularly in the oil and gas industry [2-6, 15].

Education:

  • Master of Science (M.S), Electrical Engineering and Computer Science (Machine Learning & Data Science) - University of Southern California (USC) [3, 7-14]
  • Bachelor of Technology (B.Tech.), Electronics and Communications Engineering - Gujarat Technological University (G.T.U.)- BVM Engineering College [3, 8-13, 16, 17]

Skills:

  • Programming Languages: Python, Java, C++, SQL, Scala, MATLAB, Bash/Shell Scripting, JavaScript, TypeScript, R, C#, Kotlin [17-21]
  • AI/ML Frameworks: PyTorch, TensorFlow, Hugging Face, SpaCy, OpenCV, Keras, Scikit-learn, XGBoost, LightGBM, LangChain, LlamaIndex [17, 20, 21]
  • Machine Learning: Supervised Learning, Unsupervised Learning, Deep Learning, Feature Engineering, Model Training, Model Validation, Model Optimization, Hyperparameter Tuning, Regression, Classification, Clustering, Reinforcement Learning, Sentiment Analysis, Generative AI, Predictive Maintenance, Condition Monitoring [11, 13, 16, 19-21]
  • NLP/LLMs: Transformer Models (BERT, GPT, T5), Retrieval-Augmented Generation (RAG), LlamaIndex, Cohere API, OpenAI API, LangChain, Text Analytics [18-25]
  • Computer Vision: CNNs, YOLO, ResNet, U-Net, Image Segmentation, Object Detection, OpenCV, Image Augmentation [18-21, 26-28]
  • Big Data & Cloud: Apache Spark, Hadoop, Distributed File Systems, Data Ingestion, ETL, Data Pipelines, AWS (SageMaker, EC2, S3, Lambda), GCP, Azure, Databricks, Snowflake, MongoDB, DynamoDB, Kafka [13, 19-21, 25]
  • DevOps & MLOps: Docker, Kubernetes, Jenkins, Terraform, MLflow, Kubeflow, Triton, GitHub Actions, CI/CD Pipelines, TensorBoard, Git/GitHub, MLflow, TensorBoard [18-24, 29]
  • Databases: SQL Server, MySQL, PostgreSQL, MongoDB, Cassandra, Vector Databases (FAISS, Pinecone), Elasticsearch, SQLAlchemy [20, 21, 30-32]
  • Data Analytics & Visualization: Power BI, Tableau, Grafana, Excel, ETL Pipelines, IBM SPSS, RStudio [16, 19, 31]
  • Web & API Development: React, Vue.js, Node.js, Django REST Framework, Flask, GraphQL, Spring boot, RESTful APIs [26, 27, 31, 33-40]
  • Embedded Systems: QT, C++, SPI, MQTT, ARM Microprocessors, Linux BIOS Development [26, 27, 40-44]
  • Other: Agile, Gitlab VC, Microservices Architecture, Yocto Project [26, 27, 40, 43, 45-47]

Professional Experience (Most Recent):

  • AI/ML Engineer | Everly HSE Corp [48, 49] (Nov 2024 – Present)
  • Machine Learning Engineer | Bear Brown & Company [33-39] (Mar 2024 - Present)
  • Software Engineer - OS, Product, & Firmware | BlackPearl Tech. [26, 27, 40, 42-44, 50, 51] (Aug 2023 - Mar 2024)
  • ML Software Engineer | MetLife [47, 52-55] (May 2022 – Jul 2023)
  • Machine Learning Engineer - ML, DL, Data Science, GIS | BISAG-N [28, 57-61] (Dec 2020 - Aug 2021)

Projects:

  • Text-to-Video Generation with Diffusion Models: Developed an AI-powered text-to-video generation system using Diffusers and Hugging Face models, achieving a 50% reduction in processing times with GPU acceleration [31, 62-64].
    • Skills: Large Language Models (LLMs), Diffusion Models, Prompt Engineering, Machine Learning, GPU Acceleration, MLOps [31, 62-64].
    • GitHub: [Text-to-Video Diffusion Magic]
  • Automated Personalized Job Application Generator: Built an AI-powered system for auto-generating personalized job application emails using RAG and LLMs with Weaviate for vector embeddings [65-67].
    • Skills: Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Weaviate, Resume Parsing, API Automation [65-67].
  • SVM Dual Formulation & Kernel Comparison: Created Python scripts utilizing Support Vector Machines (SVM) with dual formulation and kernel tricks, achieving 85% precision [68, 69].
    • Skills: SVM, Kernel Methods, Python, Scikit-learn, Data Visualization, Machine Learning [68, 69].
    • GitHub: [SVM Dual Formulation & Kernel Comparison]
  • Gene-disease association Prediction using Graph Neural Networks (GNNs): Developed predictive models for gene-disease associations using GAT and GraphSAGE architectures, achieving high AUC-ROC and F1 scores [70-73].
    • Skills: Graph Neural Networks, Attention Mechanisms, Large-scale Data Analysis, Machine Learning, Graph Embeddings [70-73].
    • GitHub: [Gene Disease Association Prediction]
  • Probabilistic Simulations & Monte Carlo Experiments: Built Python simulations to model statistical concepts and applied machine learning principles [74-77].
    • Skills: Python, Monte Carlo Methods, Statistical Modeling, Data Visualization, Machine Learning [74-77].
    • GitHub: [Probabilistic Simulations]
  • Distributed File System with MySQL Emulation for Foreclosure Analysis: Created a MapReduce partition strategy to speed up data processing and analyzed foreclosure rates [32, 78].
    • Skills: Distributed Systems, SQLAlchemy, MySQL, MapReduce, Data Analytics, AWS [32, 78].
    • YouTube: [Foreclosure Analysis Video]
  • American Sign Language Recognition using Deep Learning: Developed CNN models and ResNet50 for real-time sign language recognition, achieving high accuracy [12, 69, 79, 80].
    • Skills: Deep Learning, PyTorch, Convolutional Neural Networks (CNNs), Computer Vision CV, ResNet50, TensorFlow, Data Analytics [12, 69, 79, 80].
    • GitHub: [ASL Reader Project]
  • RAG-based AI Chatbot for Customer Service: Designed a RAG chatbot using LangChain, LlamaIndex, and OpenAI’s GPT APIs, reducing query resolution time [77].
    • Skills: Retrieval-Augmented Generation, CodeLlama [77].

Certifications:

  • Microsoft Certified: Azure AI Engineer Associate [12, 80-84]
  • Career Essentials in Generative AI | Microsoft [12, 80-85]
  • Vector Databases Professional Certificate by Weaviate | Weaviate [12, 80, 81, 86, 87]
  • Anaconda Python for Data Science Professional Certificate | Anaconda [12, 80-83, 87]
  • Docker Foundations Professional Certificate | Docker [12, 80, 81, 83, 86, 87]
  • Generative AI, Microsoft Copilot for productivity | Microsoft [12, 80, 84]
  • Responsible AI and use of Generative AI for creative solutions | Microsoft [12, 80, 86, 87]
  • Advanced AI: Transformers for Computer Vision | LinkedIn [12, 80, 87]
  • Machine Learning with Python: Foundations | LinkedIn [12, 80, 81, 87]
  • Research HIPAA, Biomedical Human Subjects, GCP | CITI Program [12, 80, 87]
  • AWS Certified Machine Learning– Specialty (Expected: 12/2024 or 02/2025) [12, 87]

Reach Me:

Pinned Loading

  1. LA_Foreclosure_Rates_Analysis LA_Foreclosure_Rates_Analysis Public

    Incremental Analysis on Foreclosure Rates for City of Los Angeles

    Jupyter Notebook

  2. Gene_Disease_Association_Prediction_with_GAT Gene_Disease_Association_Prediction_with_GAT Public

    This project focuses on analyzing and predicting Gene-Disease Associations (GDA) using graph-based machine learning techniques. It leverages curated datasets, protein-protein interaction (PPI) data…

    Jupyter Notebook 1

  3. AmericanSignLanguage_Reader AmericanSignLanguage_Reader Public

    Project Data: The Dataset is 1.11 GB in Size. The images in the dataset are manually captured and not computer-generated. The dataset linked above contains images from 29 classes (26 alphabets, SPA…

    Jupyter Notebook

  4. ForestFires_Prediction ForestFires_Prediction Public

    This dataset contains weather data from 2 regions in Algeria over the period of 3 months and the goal is to predict if a fire occurred at any day within that period. To create a real-world scenario…

    Jupyter Notebook

  5. CampusNavigation CampusNavigation Public

    final-project-RudraxDave created by GitHub Classroom - This project focuses on using data structures in C++ and implementing various graph algorithms to build a map application.

    C++

  6. LostNFound LostNFound Public

    Lost-and-Found Application for University Operations -Solved problem of Lost or Recovered items with features such as image uploads, image recognition, and smart matching with technologies like And…

    5 3