AI Engineer | Computer Vision Engineer | Machine Learning Engineer

Building practical AI solutions through Machine Learning, Computer Vision, and intelligent information retrieval.

GitHub • LinkedIn • Email

I'm a Computer Science graduate passionate about building AI-powered solutions that address real-world problems. My interests and hands-on experience span Machine Learning, Deep Learning, Computer Vision, Natural Language Processing, and AI application development.

During my Computer Vision internship at Verifygn Tech, I worked on industrial Computer Vision workflows involving dataset preparation, large-scale image annotation, preprocessing, model training, evaluation, and optimization. I gained practical exposure to object detection models and computer vision pipelines for automotive and manufacturing datasets.

Beyond model development, I enjoy exploring how AI techniques can be integrated into useful applications. My projects include document intelligence and Retrieval-Augmented Generation (RAG), automated grievance classification, intrusion detection, environmental prediction, and image classification.

Currently, I'm developing RAGNYS — Refining Information into Knowledge, a document intelligence project that combines structured document processing, semantic retrieval, vector embeddings, and locally hosted Large Language Models to support natural-language question answering.

I'm looking for opportunities where I can contribute as an AI Engineer, Computer Vision Engineer, or Machine Learning Engineer, while continuing to deepen my skills in building reliable and practical AI systems.

• Python
• Java
• C
• SQL

• Machine Learning
• Deep Learning
• Computer Vision
• Natural Language Processing
• Convolutional Neural Networks (CNNs)
• LSTM Networks
• Feature Engineering
• Model Training, Evaluation & Optimization

• TensorFlow
• Keras
• Scikit-learn
• OpenCV
• Ultralytics YOLO (YOLOv8 / YOLO11)
• Pandas
• NumPy
• Sentence Transformers

• Retrieval-Augmented Generation (RAG)
• Text Embeddings
• Semantic Search
• Vector Search
• Large Language Model Integration
• Ollama
• pgvector

• FastAPI and Flask — backend technologies used in projects
• PyQt — desktop application development
• React and TypeScript — frontend technologies used in RAGNYS
• PostgreSQL, MySQL, and MongoDB
• Git and GitHub
• Docker and Docker Compose
• Roboflow
• Jupyter Notebook

AI-Powered Document Intelligence & Retrieval-Augmented Generation

An AI-powered document intelligence application designed to transform complex PDF documents into structured, searchable knowledge and support natural-language question answering through semantic retrieval and locally hosted language models.

Key Highlights

• Developed a document-processing workflow involving PDF parsing, text cleaning, document structure analysis, layout-aware grouping, and knowledge unit generation.
• Integrated Sentence Transformers using sentence-transformers/all-MiniLM-L6-v2 to generate 384-dimensional text embeddings.
• Used PostgreSQL with pgvector for vector storage and semantic similarity search.
• Integrated Ollama with the qwen3.5:4b model for local LLM inference.
• Worked with a FastAPI-based backend and React-based interface to connect document-processing and retrieval workflows.
• Explored the integration of document intelligence, semantic retrieval, and LLM-based answer generation in a single application.

Technologies: Python, Sentence Transformers, RAG, Embeddings, Semantic Search, PostgreSQL, pgvector, Ollama, FastAPI, React, TypeScript.

Repository: Explore RAGNYS

Computer Vision Internship

Worked on Computer Vision workflows for industrial automotive and manufacturing datasets, with a focus on data preparation, object detection, and model evaluation.

Key Highlights

• Annotated 10,000+ images across industrial datasets.
• Performed bounding-box and region-of-interest annotation.
• Worked on image preprocessing and dataset preparation.
• Gained hands-on exposure to YOLO-based object detection, transfer learning, and custom model architectures.
• Contributed to model evaluation, confidence-score validation, and hyperparameter tuning.

Technologies: Python, OpenCV, YOLOv8, YOLO11, Roboflow, Deep Learning.

A full-stack application that uses Machine Learning and Natural Language Processing to classify complaints, route them to appropriate departments, and automate email notifications.

Key Highlights

• Implemented text classification using TF-IDF vectorization and a trained Machine Learning classifier.
• Automated department prediction and complaint routing.
• Integrated MongoDB for data persistence.
• Incorporated SMTP-based email notifications and complaint-tracking workflows.

Technologies: Python, Flask, Scikit-learn, TF-IDF, MongoDB, SMTP.

Developed a hybrid Machine Learning and Deep Learning framework combining feature selection and fuzzy logic to improve intrusion detection performance.

Reported Results

• 98.4% detection accuracy.
• 18% reduction in false positives.

Focus Areas: Machine Learning, Deep Learning, Cybersecurity, Feature Selection, Fuzzy Logic.

Developed an AI-powered desktop application that predicts rainwater quality, estimates harvestable rainwater quantity, and calculates rain-related energy potential using multiple Machine Learning models.

Key Highlights

• Integrated multiple predictive models into a unified application.
• Developed a desktop interface using PyQt.
• Combined environmental data analysis with predictive modeling.

Technologies: Python, Scikit-learn, Machine Learning, PyQt, Pandas.

Implemented and compared multiple Machine Learning and Deep Learning models for automated rice variety classification using image-processing techniques.

Focus Areas: Image Processing, Feature Extraction, Classification, Model Evaluation.

• Applied Artificial Intelligence
• Computer Vision & Object Detection
• Machine Learning & Deep Learning
• Generative AI & Retrieval-Augmented Generation
• Document Intelligence & Semantic Search
• AI-Powered Application Development
• Intelligent Automation

I'm open to opportunities in AI Engineering, Computer Vision, and Machine Learning, where I can contribute to meaningful projects and continue growing as an engineer.

• LinkedIn: linkedin.com/in/gowthamravuri
• GitHub: github.com/GowthamRavuri01
• Email: gowthamravuri01@gmail.com

Turning ideas into intelligent, practical solutions.