Aniket Kharose aniketkharose

👋 Hey, I'm Aniket

Final-year E&TC Engineering student at SKNCOE, Pune (Savitribai Phule Pune University). I like building AI that you can actually use: apps that see faces, hear voices, watch your squat form and score your resume, all deployed and running live.

My sweet spot is where machine learning meets hardware: computer vision on one side, microcontrollers and embedded systems on the other.

class Aniket:
    role     = "AI/ML + Embedded Systems Engineer (in the making)"
    college  = "SKNCOE, Pune"
    focus    = ["Computer Vision", "Edge AI", "Embedded Systems", "NLP"]
    loves    = ["shipping real projects", "turning ideas into live demos"]
    looking  = "AI/ML and Embedded Systems internships"

🚀 Featured Projects

🧠 AI Resume ATS

Know your score before the recruiter does.

Analyzes a resume against a job description and returns an ATS score, skill matching, feedback and recommendations, with a downloadable PDF report.

FastAPI Streamlit spaCy scikit-learn Supabase

🚀 Live Demo · 💻 Code

📸 SnapClass

Say cheese. You're marked present.

AI attendance system using face recognition and voice recognition, with separate student and teacher portals.

Dlib SVM Resemblyzer Streamlit Supabase

🚀 Live Demo · 💻 Code

🏋️ AI Gym Coach

A coach that watches every rep.

Real-time pose detection with form feedback and AI voice coaching for workout tracking.

MediaPipe OpenCV Streamlit Python

💻 Code

🔬 What each project taught me

Project	The interesting challenge
🧠 AI Resume ATS	Deployed on a 512 MB free tier. Replaced a heavy transformer with a lightweight TF-IDF matcher to stop out-of-memory crashes.
📸 SnapClass	Combined two biometric pipelines: Dlib embeddings with an SVM for faces, and Resemblyzer embeddings for voices.
🏋️ AI Gym Coach	Turned live pose landmarks into real-time form feedback and spoken coaching.

🧭 The Pipeline I Keep Building

flowchart LR
    A["💡 Idea"] --> B["📊 Data and Models"]
    B --> C["🧠 AI Pipeline"]
    C --> D["🖥️ Streamlit App"]
    D --> E["☁️ Deploy"]
    E --> F["🔁 Debug and Improve"]
    F --> C

🔮 Where I'm Heading: Edge AI

flowchart LR
    S["🔌 Sensors"] --> M["⚙️ ESP32 / Microcontroller"]
    M --> I["🧠 On-device ML Inference"]
    I --> A["📱 Real-world Action"]

Right now I'm exploring how to run ML models directly on ESP32 and microcontrollers, bringing together my E&TC background and my AI work.

🛠️ Tech Stack

Languages and Core

ML, DL and Vision

Backend, Apps and Data

Embedded and Hardware

Tools

💬 Ask Me About

👁️ Computer vision, face and pose detection
🧠 Building and deploying ML pipelines end to end
🔌 Embedded systems and Edge AI on ESP32
☁️ Getting apps to run inside free-tier limits

📊 GitHub Stats

📫 Let's Connect

I'm actively looking for AI/ML and Embedded Systems internships. If you're building something in computer vision, Edge AI or embedded AI, I'd love to chat.

⭐ If you like my projects, a star goes a long way!

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