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Srujanx/README.md

Hey, I'm Srujan

AI Engineer | ML Enthusiast | Building Production-Grade AI Systems

Typing SVG

LinkedIn Email GitHub

💫 About Me

Coding

name: Srujan
located_in: Toronto, Ontario 🇨🇦
current_role: Third-year AI Engineering Student
education: Durham College | GPA 4.0/5.0
status: Actively seeking Summer 2026 AI/ML Internships

fields_of_interest:

  • Machine Learning Engineering
  • Natural Language Processing
  • Computer Vision
  • MLOps & Production Systems

currently_learning:

  • Advanced MLOps & CI/CD
  • Scalable ML Architectures
  • Model Optimization Techniques

philosophy:
"Great AI isn't just accurate—it's fast, scalable, and solves real problems in production."


What I Do

Build Production-Ready ML

I don't just train models—I ship them. From research papers to deployed APIs with proper error handling, monitoring, and optimization.

My Stack:

  • Deep Learning (PyTorch, TensorFlow)
  • NLP (Transformers, FinBERT, Pegasus)
  • Computer Vision (CNNs, Transfer Learning)
  • FastAPI, Docker, AWS

Measure Everything

If it can't be quantified, it can't be improved. I obsess over metrics:

  • Model accuracy & performance
  • Inference latency optimization
  • Memory footprint reduction
  • Real-world impact metrics

Results I'm proud of:

  • 93% classification accuracy
  • Sub-2s response times
  • 80% API call reduction

Tech Arsenal

🤖 Machine Learning & AI

Python PyTorch TensorFlow Keras scikit-learn OpenCV HuggingFace

Data Science & Analytics

Pandas NumPy Plotly Matplotlib Scipy

MLOps & Deployment

FastAPI Flask Streamlit Docker Kubernetes AWS Git MLflow

Databases & Cloud

SQL NoSQL Snowflake CUDA

Featured Projects

Financial Sentiment Dashboard

GitHub

Real-time NLP for financial markets

Impact:

  • Hours → Seconds analysis time
  • 85%+ sentiment accuracy
  • Sub-2s response time
  • 99.5% uptime

Tech: FinBERT FastAPI Docker Streamlit

Text Summarizer Pipeline

GitHub

Production ML with explainable AI

Impact:

  • 15% better than baseline
  • 4x throughput improvement
  • SHAP interpretability
  • Modular 5-stage pipeline

Tech: Pegasus PyTorch SHAP MLOps

Image Classification System

GitHub

CNN with transfer learning

Impact:

  • 93% accuracy (36 classes)
  • 8% over baseline
  • <2s real-time predictions
  • 15% overfitting reduction

Tech: TensorFlow Keras CNNs Transfer Learning

Why These Projects Stand Out

Production-Ready Code

  • Proper error handling
  • Comprehensive logging
  • Optimized for deployment

Measurable Impact

  • Quantified improvements
  • Performance benchmarks
  • Real-world applicability

End-to-End Solutions

  • From data → deployed API
  • Not just Jupyter notebooks

GitHub Stats:



Certifications & Learning

** Machine Learning**

  • Supervised ML: Regression & Classification
    • DeepLearning.AI & Stanford

** Data Science**

  • Exploratory Data Analysis
    • IBM
  • Python Data Structures
    • University of Michigan

** Specialized Training**

  • Ultimate Data Bootcamp 2.0
    • 5 Minutes Engineering
  • IIT Guwahati Data Science Course

Random Dev Quote


Let's Connect!

Open to Summer 2026 AI/ML Internship Opportunities

I'm passionate about building ML systems that ship to production and create real-world impact. Let's collaborate!

LinkedIn Email Portfolio


"Building AI systems that create measurable impact, one model at a time"


Profile Views

Pinned Loading

  1. financial-sentiment-dashboard financial-sentiment-dashboard Public

    Analyzing apple stocks [AAPL] with respect to everyday sentiment analysis on news and articles. This fetches news live and can be refreshed to get latest news across the globe. The application prov…

    Python 5

  2. Text-Summarizer-Project Text-Summarizer-Project Public

    Jupyter Notebook 5