John Sudhakar Kanajam

Computer Science student · Data Engineering & AI

Guntur, Andhra Pradesh, India

@john_sudhakar_kanajam

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About

Computer Science student specializing in data engineering with AI, with hands-on experience in backend development and full-stack projects. Skilled in building data pipelines, REST APIs, and RAG-based AI systems using FastAPI, PostgreSQL, and MongoDB.

Experience

Software Developer Intern

Yuga Yatra Private Limited

Feb 2026 – Apr 2026

Education

2027

B.Tech

Koneru Lakshmaiah University

Computer Science and Engineering · 2023 – 2027

Computer Science and Engineering · 2023 – 2027
2023

Intermediate

Narayana Junior College

MPC · 2021 – 2023

MPC · 2021 – 2023

Skills

<3 months

SQL RAG Python Django FastAPI Pydantic LangChain LangGraph Deep Learning Machine Learning JWT Authentication Django Rest Framework API Integration Understanding Generative AI (ChatGPT, Copilot, Gemini)

Tools / apps / platforms

<3 months

Git Redis Chroma Claude GitHub MongoDB Postman VS Code Databricks PostgreSQL Google Colab GitHub Copilot

Projects

Big Data Athlete Performance Analysis

React + Vite, FastAPI, MongoDB, REST APIs, JWT, bcrypt, SMTP, Redis

Built a full-stack analytics platform with 10+ secured REST APIs and role-based access control, managing performance data for 30+ athletes using FastAPI and MongoDB. Improved data pipelines to process and analyze 1000+ records from large CSV datasets using Python and MongoDB, with Redis-based rate limiting handling 80+ requests for reliable system performance. Integrated SMTP-based email notifications and bcrypt password hashing for secure reporting and authentication workflows, validated through 30+ test cases in Postman.
AgriSupport — AI-Based Smart Farming Platform

React + Vite, Tailwind CSS, FastAPI, PostgreSQL, Machine Learning, JWT, RAG, ChromaDB, Groq LLM

Developed a full-stack AI-powered smart farming platform offering crop recommendation, fertilizer recommendation, drought prediction, and multilingual farmer assistance, achieving 93% prediction accuracy using FastAPI and machine learning. Designed and implemented a RAG-based agricultural assistant using Groq LLM and ChromaDB vector database, retrieving from a knowledge base of 200+ documents to deliver context-aware answers to farming queries. Integrated JWT authentication, role-based admin CRUD operations, nutrient analysis, and pest detection modules across 10+ secure API endpoints, using bcrypt and Pydantic for data validation.

Courses & certifications

Oracle Agentic AI Foundations Associate Certification · 2026
MongoDB Python Developer Certification · 2026

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