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RoushanKhalid/README.md

Sk. Roushan Khalid

AI/ML Engineer | Applied AI Systems | Backend & LLM Engineering

Building practical AI systems with focus on backend engineering, applied machine learning, and real-world AI workflows.

📊 GitHub Overview

🧠 Professional Summary

AI/ML Engineer focused on building applied machine learning systems, backend APIs, and LLM-powered applications. Experienced in working with NLP, computer vision, RAG pipelines, and AI-assisted decision systems.

Interested in translating AI models into usable software systems with attention to scalability, reliability, and practical deployment.

🧭 What I Work On

  • Applied AI systems for real-world use cases
  • Backend development for AI-powered applications
  • LLM integration and RAG-based workflows
  • Multi-agent system design and orchestration
  • Computer vision and NLP-based solutions
  • AI-assisted automation systems

🛠 Technical Expertise

Backend & Engineering

  • Python, C/C++
  • FastAPI, Flask
  • REST API development
  • PostgreSQL, MySQL
  • Docker, Git, GitHub

AI / ML Stack

  • PyTorch, TensorFlow
  • Scikit-learn
  • Hugging Face ecosystem
  • LangChain, LangGraph
  • RAG systems
  • NLP & Computer Vision fundamentals

Data & Tools

  • Pandas, NumPy
  • FAISS (vector search basics)
  • ML experimentation workflows
  • Roboflow
  • Automation tools (n8n, Zapier, Make)

🎯 Applied AI Focus

  • Designing practical AI-driven systems
  • Building API-first AI applications
  • Experimenting with RAG and retrieval systems
  • Working on structured AI workflows
  • Exploring production-oriented AI engineering concepts

⚙️ Engineering Approach

Principle Description
Build First Learn by implementing real systems
Keep Simple Avoid unnecessary complexity
Iterate Fast Improve through continuous refinement
Be Practical Focus on real-world usability
Stay Reliable Prioritize stable and maintainable systems

📚 Currently Exploring

  • Production LLM systems
  • Scalable RAG architectures
  • Multi-agent AI workflows
  • AI automation pipelines
  • Backend optimization for AI systems

🏆 Highlights

  • Active in AI/ML engineering and applied research
  • Experience with real-world AI system development
  • Competitive programming background (300+ problems solved)
  • Participation in national and international tech events
  • Academic focus in Data Science and Software Engineering

🤝 Community & Leadership

  • AI/ML community engagement and mentoring activities
  • Participation in technical workshops and sessions
  • Volunteer experience in public initiatives

📫 Contact

Learning by building systems that solve real-world problems.

Pinned Loading

  1. Gesture-Controller-for-Hill-Climb-Racing-Lite Gesture-Controller-for-Hill-Climb-Racing-Lite Public

    The Gesture Controller for Hill Climb Racing Lite is a computer vision–based game control system that allows users to play the popular Hill Climb Racing game using hand gestures instead of a keyboa…

    Python

  2. SRS-Studio SRS-Studio Public

    A lightweight multi-agent SRS generator that turns a single software requirement into a complete markdown SRS through a full agent pipeline (planner → architect → developer → qa → reviewer → discus…

    Python 1

  3. ResumeRAG ResumeRAG Public

    ResumeRAG is a RAG-based resume analyzer. Upload any resume as a PDF, then ask natural-language questions such as skills, experience, education, projects, certifications and get accurate, sourced a…

    Python

  4. Question-Answering-System Question-Answering-System Public

    An intelligent semantic question-answering system built with BERT that understands intent, not just keywords. It delivers accurate answers with confidence scores through a modern, responsive UI. Op…

    HTML

  5. SkyScout-AI SkyScout-AI Public

    SkyScout AI is a smart weather companion that transforms how you plan activities. Here you'll get AI-powered recommendations perfectly adapted to current weather conditions.

    Python

  6. Smart-Campaigner Smart-Campaigner Public

    A smart customer engagement system built with Flask and RFM analysis. It segments customers into Bronze, Silver, Gold, and Platinum groups and sends personalized email offers based on their shoppin…

    Jupyter Notebook