Java Backend Developer | Full-Stack Software Engineer
B.Tech in Electronics & Communication Engineering — Banasthali Vidyapith
Machine Learning & Computer Vision Research Exposure (NIT Kurukshetra Internship & Final Year Project)

I am a software engineer focused on building robust, secure backend systems in Java & Spring Boot and dynamic full-stack applications with React.js / Next.js. I emphasize writing clean, testable code, enforcing strong authentication & authorization boundaries, designing relational database schemas, and applying efficient Data Structures & Algorithms to solve practical engineering problems.

• 🌐 Portfolio Website: 123angmish.github.io/angel-mishra-portfolio
• 🎯 Current Focus: Building secure RESTful APIs, Spring Security authentication architectures, and database concurrency & transaction management.
• 💼 Target Roles: Java Backend Developer, Full-Stack Developer, Software Development Engineer (SDE).
• 🧠 Core CS: Data Structures & Algorithms, Object-Oriented Programming (OOP), Database Management Systems (DBMS), Operating Systems, Computer Networks.

Modern developer portfolio website with interactive project showcases, constellation navigation, and full responsiveness.

• Features: Responsive design, constellation interactive nodes, research details, downloadable resume, and static production export.
• Stack: Next.js 16, React 19, TypeScript, Tailwind CSS, GitHub Pages.

Full-stack campus utility sharing platform built with Java 21, Spring Boot 3.5, React 19, and PostgreSQL.

• Concurrency & Locking: Implemented pessimistic database row-locking (SELECT FOR UPDATE) and optimistic versioning to prevent double-booking collisions during concurrent rental requests.
• Security & Authorization: Stateless JWT authentication paired with rotating HttpOnly refresh tokens; backend-enforced ownership checks on all listing mutations.
• Payment Architecture: Razorpay order workflow with server-side HMAC-SHA256 signature verification and an idempotent durable webhook inbox for reliable payment settlement.
• Stack: Java 21, Spring Boot 3.5, React 19, PostgreSQL 16, Flyway, Spring Security, Docker.

Full-stack job portal and talent acquisition platform with role-based access control and live ATS workflow.

• Security Hardening: Stateless JWT authentication with externalized secret configuration, BCrypt salt hashing, in-memory rate limiting against brute-force attacks, and server-side IDOR protection across jobs and applicant data.
• Secure Messaging & ATS: Candidate-employer communication channel with JWT-derived identity verification and an ATS pipeline (APPLIED ➔ SHORTLISTED ➔ INTERVIEW ➔ HIRED/REJECTED).
• Stack: Java 21, Spring Boot 3.5, React 18, Vite, Redux Toolkit, Tailwind CSS, Spring Security 6.

Multimodal video analytics engine and regression benchmark evaluating video pacing across YouTube Shorts, TikTok, and Instagram Reels.

• Feature Extraction: Extracted optical motion flow (0–3s hook intensity) via OpenCV and acoustic RMS energy peaks via Librosa.
• Machine Learning Benchmark: Baseline regression comparison on a controlled synthetic multimodal benchmark ($N=10,000$) where Linear Regression proved optimal ($R^2 = 0.8824$, $\text{RMSE} = 4.5194$) due to the underlying linear target structure.
• Project Evolution: Originated as a Flask research prototype during my NIT Kurukshetra internship, later independently expanded into a FastAPI + Next.js 14 platform with automated platform blueprints.
• Stack: Python, FastAPI, Next.js 14, OpenCV, Librosa, Scikit-Learn, Tailwind CSS.

AI and Computer Vision prototype developed as a Final Year Project in B.Tech ECE (Banasthali Vidyapith) for flood-scene analysis, estimated surface motion/flow analysis, and community risk alerting.

• Computer Vision Pipeline: Combines a MobileNetV2-based CNN for frame-level flood scene classification with Lucas–Kanade optical flow for relative surface motion tracking and estimated flow analysis.
• Verified Architecture: Saved model contains a MobileNetV2-based frozen backbone (~2.26M non-trainable parameters) with a custom dense binary classification head (~164K trainable parameters), plus a separate reproducible retraining module under training/.
• Telemetry & WebSockets: Low-latency video stream pipeline with MJPEG feed and WebSocket channels for location-scoped emergency telemetry.
• Stack: Python 3.10+, FastAPI, OpenCV, MobileNetV2-based CNN (TensorFlow), SQLite, WebSockets.

Full-Stack AI culinary platform powered by Spring Boot and Google Gemini multimodal vision for fridge-scanning, recipe generation, and voice workflows.

• Stack: Java, Spring Boot, Google Gemini API, Web Speech API.

Empathetic support platform featuring AI-assisted reflection, therapeutic recovery tools, and voice journaling.

• Stack: Java, Maven, Google Gemini, OpenAI, Web Audio API.

• Research Intern — National Institute of Technology (NIT), Kurukshetra
  • Researched Computer Vision and Machine Learning algorithms for multimodal feature extraction, optical flow motion analysis, and audio signal processing.
  • Built the initial research prototype in Python/Flask that laid the algorithmic foundation for the AI Virality Predictor.

• Actively practicing Data Structures & Algorithms (Arrays, Hash Tables, Trees, Graphs, Dynamic Programming, Two Pointers).
• Object-Oriented Design (SOLID principles, Factory, Strategy, and Singleton design patterns).
• Relational database schema design, indexing, transactions (ACID properties), and SQL optimization.

• 🌐 Live Portfolio: 123angmish.github.io/angel-mishra-portfolio
• 📧 Email: angelmishraofficial@gmail.com
• 💼 LinkedIn: linkedin.com/in/angel-mishra-992474345
• 🐙 GitHub: github.com/123angmish