Vijay Manda
Professional Summary
AI/ML Engineer with 0 years of professional experience and internship experience building full‑stack AI applications. Experienced in Python-based model development (PyTorch), computer vision (YOLOv8, OpenCV), and retrieval-augmented generation (RAG) for contextual insights. Built and deployed REST inference endpoints using FastAPI and Node.js/Express, and delivered responsive model-driven interfaces with React, HTML, and CSS. Worked with MongoDB and MySQL for structured and document storage and used Git, Postman, Render and Vercel for CI and deployment. Proven ability to take ML prototypes through data preprocessing, model training, inference optimization, and production integration during an AI startup internship and multiple academic projects.
Technical Skills
Work Experience
- Designed RESTful inference endpoints using FastAPI to serve computer vision models and enable predictable request/response behavior for frontend clients.
- Implemented Node.js and Express.js admin APIs to ingest application telemetry and manage user metadata for the product backend.
- Developed React-based UI components and responsive pages with HTML/CSS to display real-time model outputs and interactive result visualizations.
- Built and trained computer vision models in Python using YOLOv8 and PyTorch; improved detection robustness via augmentation and preprocessing pipelines.
- Integrated MongoDB to persist analysis results and MySQL for relational user records, and authored API contracts for secure data flow between frontend and models.
- Optimized model inference pipelines and pre/post-processing code using OpenCV and NumPy to reduce end-to-end latency for the wound analysis feature.
Projects
- Developed modular Python tools that use the GitHub API and MCP to automate repository, branch, commit, pull request, and issue operations.
- Implemented automation flows to allow AI agents to perform routine repository maintenance tasks, improving reproducibility of CI steps.
- Integrated Git operations and API calls with error handling and logging to ensure reliable automated workflows across repositories.
- Built a Flask backend and React frontend to collect profile signals and present real-time fraud likelihood for social media accounts.
- Engineered features from user metadata and behavior, and trained classification models using scikit-learn to detect suspicious accounts.
- Deployed API endpoints for on-demand profile checks and visualized probability scores and feature contributions in the UI.
- Implemented CNN-based object and scene detection using YOLOv8 and PyTorch to extract actionable information from image/video frames.
- Integrated a retrieval-augmented generation (RAG) component to generate contextual insights and recommendations from detection outputs.
- Optimized preprocessing and inference code paths with OpenCV and batch processing to improve throughput and accuracy of detection pipelines.
Education
Certifications
Achievements
- 1st Rank — India's Largest Cloud Developer Program: Ranked first in the Cloud Developer Program conducted by 3SVK JNTUH and earned a Pre-Placement Interview (PPI).
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