👨‍💻 Kiran Mondal | Full-Stack Developer & AI/ML Engineer

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🚀 About Me

Senior BCA student specializing in Computer Science & AI, with expertise in building scalable backend systems, intelligent automation, and enterprise-grade applications. Passionate about continuous learning and advancing my skills across multiple programming languages and technologies.

• 🔬 Specialization: AI/ML Systems, Backend Architecture, Cybersecurity
• 💼 Focus Areas: Distributed Systems, Real-time Applications, Computer Vision
• 🎯 Current Mission: Developing production-grade solutions with Python, Go, and Rust
• 🔐 Interest: Network Security, Penetration Testing, System Optimization
• 📈 GitHub Stats: 50+ Projects | 1K+ Followers | 10K+ Contributions

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🛠️ Technical Arsenal

Core Languages

AI/ML & Data Science

Backend & Frameworks

Databases & Cache

Cloud & DevOps

Tools & Version Control

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🏗️ Featured Projects

1. Exam Hall Monitoring System

Computer Vision | Real-time Detection | Security

• Advanced behavioral detection system using Python & OpenCV
• Real-time monitoring with anomaly flagging capabilities
• ML-powered proctoring solution for educational institutions
• Impact: Deployed in 5+ institutions, monitoring 1000+ exams
• Tech Stack: Python, OpenCV, PyTorch, FastAPI, PostgreSQL
• 🔗 Repository | 📊 Stats

2. WatchWiz & Movie Hunter

Telegram Automation | Bot Architecture | Microservices

• Production-grade Telegram bots for content discovery and group management
• Event-driven architecture with async processing
• 10K+ active users with 99.9% uptime SLA
• Performance: <100ms response time, 50+ concurrent connections
• Tech Stack: Python, Telethon, FastAPI, Redis, MongoDB
• 🔗 Repository | 📱 Try it

3. AI Recommendation Engine

Machine Learning | Personalization | Backend Architecture

• Intelligent recommendation system with hybrid filtering
• Collaborative & content-based filtering integration
• REST API with optimized query performance
• Metrics: 95%+ accuracy, <50ms query response
• Tech Stack: Python, Scikit-learn, PostgreSQL, FastAPI, Redis
• 🔗 Repository | 📈 Metrics

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📊 GitHub Analytics & Contributions

📈 Contribution Graph

🔝 Top Contributions

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💡 Core Competencies

Domain	Expertise
Backend Development	Microservices, REST APIs, gRPC, Real-time Systems, Database Optimization
Machine Learning	NLP, Computer Vision, Recommendation Systems, Anomaly Detection, Time Series
Cloud Architecture	Containerization, Serverless, Infrastructure as Code, Multi-cloud strategies
Security	Vulnerability Assessment, Penetration Testing, OWASP Top 10, Network Security
DevOps & CI/CD	Docker, Kubernetes, GitHub Actions, ArgoCD, Monitoring & Observability
System Design	Distributed Systems, Caching Strategies, Load Balancing, Database Sharding

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🎓 Education & Certifications

• Bachelor of Computer Applications (BCA) - Computer Science & AI Specialization
• Cloud Architect Associate - AWS Certified
• Machine Learning Fundamentals - Fast.ai Deep Learning Course
• Cybersecurity Essentials - CompTIA Security+ Equivalent

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💼 Professional Experience

Technical Expertise Highlights

🔹 Backend Systems
   └─ Built microservices handling 10K+ RPS
   └─ Implemented caching layers reducing latency by 60%
   └─ Designed event-driven architectures with message queues

🔹 Machine Learning
   └─ Deployed ML models in production with 95%+ accuracy
   └─ Optimized inference latency to <50ms
   └─ Built recommendation engines processing 1M+ daily interactions

🔹 DevOps & Infrastructure
   └─ Orchestrated containerized applications with Kubernetes
   └─ Set up CI/CD pipelines reducing deployment time by 75%
   └─ Implemented monitoring & alerting with Prometheus & Grafana

🔹 Security & Compliance
   └─ Conducted penetration testing on web applications
   └─ Implemented OAuth 2.0 & JWT authentication
   └─ Ensured GDPR & data protection compliance

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🚀 Current Learning & Skill Development

Programming Language Progression

📊 Language Proficiency Matrix:

┌────────────────────────────────────────────────────────────────��┐
│ Language      │ Level          │ Status                          │
├─────────────────────────────────────────────────────────────────┤
│ Python        │ ⭐⭐⭐⭐⭐ Expert    │ ✅ Production-Ready             │
│ Go            │ ⭐⭐⭐⭐ Advanced   │ 🎯 Intermediate + Moving Up     │
│ JavaScript    │ ⭐⭐⭐ Intermediate│ ✅ In Active Development        │
│ TypeScript    │ ⭐⭐⭐ Intermediate│ ✅ In Active Development        │
│ Rust          │ ⭐⭐ Beginner+   │ 📚 Learning & Exploring         │
│ Ruby          │ ⭐⭐ Beginner+   │ 📚 Planning to Learn Soon       │
│ Swift         │ ⭐⭐ Beginner+   │ 📚 Planning to Learn Soon       │
│ SQL           │ ⭐⭐⭐⭐ Advanced   │ ✅ Production-Ready             │
└─────────────────────────────────────────────────────────────────┘

Legend:
  ✅ Production-Ready    - Used in real projects & deployments
  🎯 In Development      - Active learning with practical projects
  📚 Learning Phase      - Studying concepts & building foundations

What I'm Currently Working On

🔹 Go (Advanced → Intermediate+)

• Current Focus: Building microservices and concurrent systems
• Projects in Progress:
  • Distributed cache system with Go
  • High-performance REST API service
  • gRPC service implementation
• Timeline: Mastering advanced patterns by Q3 2026
• Expected Level: Senior-level Go developer

🔹 Python (Expert)

• Current Focus: ML/AI models, system optimization
• Active Projects: Exam monitoring, recommendation engines
• Role: Primary language for AI/ML and backend development
• Expertise: FastAPI, Django, Data Science, Automation

🔹 JavaScript & TypeScript (Intermediate)

• Current Focus: Frontend development and Node.js backends
• Recent Projects: React components, Next.js applications
• Development: Continuously improving full-stack capabilities

🔹 Rust (Beginner+ → Learning)

• Current Focus: Learning memory safety and systems programming
• Study Path:
  • ✓ Ownership and borrowing concepts
  • → Working on: Async/await patterns
  • ⏳ Next: Building CLI tools & backend services
• Timeline: Reach intermediate by Q4 2026
• Goals: System-level programming, performance-critical applications

🔹 Ruby & Swift (Planned)

• Ruby:

  • Interest: Rails framework, scripting automation
  • Status: Planning to start learning Q3 2026
  • Target: Web development & DevOps scripting

• Swift:

  • Interest: iOS/macOS development
  • Status: Planning to start learning Q4 2026
  • Target: Cross-platform mobile development

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🎯 Learning Roadmap & Ongoing Projects

learning_progression = {
    "current_phase": "Advanced Development",
    "active_languages": {
        "Go": {
            "status": "Intermediate (Advancing)",
            "focus": "Microservices, Concurrency, Backend Services",
            "projects": [
                "Distributed caching system",
                "High-performance REST API",
                "gRPC implementations"
            ],
            "goal": "Senior-level proficiency by Q3 2026"
        },
        "Python": {
            "status": "Expert",
            "focus": "ML/AI, Backend optimization, Automation",
            "projects": [
                "Production ML models",
                "AI recommendation systems",
                "FastAPI microservices"
            ],
            "goal": "Maintain expert-level status"
        },
        "JavaScript/TypeScript": {
            "status": "Intermediate",
            "focus": "Full-stack development",
            "projects": [
                "Next.js applications",
                "React components",
                "Node.js backends"
            ],
            "goal": "Advanced proficiency by Q3 2026"
        }
    },
    "learning_languages": {
        "Rust": {
            "status": "Beginner (Active Learning)",
            "focus": "Systems programming, Async patterns",
            "resources": [
                "The Rust Book",
                "System design patterns",
                "Performance optimization"
            ],
            "timeline": "Intermediate by Q4 2026"
        },
        "Ruby": {
            "status": "Planned",
            "focus": "Rails framework, Automation scripting",
            "timeline": "Start: Q3 2026"
        },
        "Swift": {
            "status": "Planned",
            "focus": "iOS/macOS development",
            "timeline": "Start: Q4 2026"
        }
    },
    "skill_development": {
        "distributed_systems": "Advanced patterns & system design",
        "ml_research": "LLMs, fine-tuning, multi-modal models",
        "devops": "Kubernetes advanced patterns & GitOps",
        "cloud_architecture": "Multi-cloud strategies & serverless"
    }
}

📈 Current Learning Achievements

• ✅ Completed Go fundamentals & concurrent programming
• ✅ Built production-grade microservices in Go
• ✅ Advanced Python expertise in ML/AI
• ✅ Intermediate JavaScript/TypeScript skills
• 🚀 Active Rust learning journey started
• 📚 Planning Ruby and Swift learning tracks

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🏭 ITI & Industrial Engineering Plans

Future Education: 2-Year Industrial Training Institute (ITI) Tracks

I'm planning to pursue complementary vocational training through ITI (Industrial Training Institute) to expand my skill set into practical engineering domains. This will combine my software expertise with hands-on industrial knowledge.

📌 Primary Track: Petrol (Petroleum) Engineering

• Duration: 2 years (Post-BCA)

• Start Timeline: 2026-2027

• Focus Areas:

  • Petroleum technology & extraction systems
  • Oil & gas processing
  • Refinery operations
  • Equipment maintenance & automation
  • IoT sensor integration for monitoring
  • Data analysis for production optimization

• Integration with Tech Skills:

  • Build automation systems using Python/Go
  • Real-time monitoring dashboards with React
  • ML models for predictive maintenance
  • IoT device communication & data pipelines
  • Cloud-based operational analytics

🔌 Secondary Track: Electronics Engineering

• Duration: 2 years (Post-BCA)

• Start Timeline: 2026-2027

• Focus Areas:

  • Electronics circuit design & troubleshooting
  • Industrial control systems
  • PLC (Programmable Logic Controllers) programming
  • Power systems & electrical distribution
  • Embedded systems development
  • Hardware-software integration

• Integration with Tech Skills:

  • Embedded C/C++ for microcontroller programming
  • Circuit simulation & SPICE modeling
  • Real-time control systems with Rust
  • IoT frameworks and edge computing
  • Hardware diagnostics automation

🔄 Combined Career Path: Tech × Industrial Engineering

┌─────────────────────────────────────────────────────────────┐
│ Hybrid Career Opportunities After ITI Completion           │
├─────────────────────────────────────────────────────────────┤
│                                                              │
│ 🌐 Options A: Oil & Gas Tech Specialist                     │
│    └─ Petroleum + Software Integration                      │
│    └─ IoT platforms for energy sector                       │
│    └─ Predictive analytics for extraction                   │
│                                                              │
│ 🔌 Options B: Industrial Automation Engineer                │
│    └─ Electronics + Embedded Systems                        │
│    └─ Factory automation & robotics                         │
│    └─ Control systems programming                           │
│                                                              │
│ 🏗️ Options C: Smart Infrastructure Developer               │
│    └─ Both Petrol + Electronics knowledge                   │
│    └─ Advanced IoT & edge computing                         │
│    └─ Industry 4.0 digital transformation                   │
│                                                              │
│ 💡 Options D: Innovation Consultant                         │
│    └─ Technical expertise across software & hardware        │
│    └─ System architecture & optimization                    │
│    └─ Industry modernization projects                       │
│                                                              │
└─────────────────────────────────────────────────────────────┘

🎯 2-Year ITI Timeline & Goals

Year 1 (2026-2027): Foundation & Fundamentals
├─ Q1: Enroll in ITI program (Petrol or Electronics)
├─ Q2: Master theoretical concepts & safety protocols
├─ Q3: Hands-on lab work & equipment training
├─ Q4: First practical certifications & projects

Year 2 (2027-2028): Advanced Applications & Integration
├─ Q1: Advanced technical modules & specializations
├─ Q2: Real industrial internships & placements
├─ Q3: Combined tech projects (Software + Hardware)
├─ Q4: Final certification & skill validation
└─ Outcome: Industry-ready technician + Software engineer

Post-ITI: 2028+
├─ Career: Hybrid tech-industrial roles
├─ Specialization: Automation, IoT, or Industry 4.0
├─ Advancement: Lead engineer or technical consultant
└─ Mastery: Multi-domain expert in tech + engineering

💼 Skills After ITI Completion

Skill Category	Petrol Track	Electronics Track	Both
Technical Knowledge	Oil/Gas systems	Circuit design	Industrial ops
Hands-on Skills	Equipment operation	PCB assembly	Equipment maintenance
Programming	Python automation	Embedded C	Real-time systems
Certifications	ITI Petroleum	ITI Electronics	Industry licenses
Career Level	Technician → Engineer	Technician → Engineer	Senior hybrid roles

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📈 Current Focus & Learning Path

current_roadmap = {
    "distributed_systems": {
        "learning": "Advanced Golang patterns & microservices",
        "target": "Design systems interview prep",
        "timeline": "Q3 2026"
    },
    "ml_research": {
        "learning": "Large Language Models & fine-tuning",
        "projects": ["Retrieval Augmented Generation", "Multi-modal models"],
        "timeline": "Ongoing"
    },
    "devops": {
        "learning": "Kubernetes advanced patterns & GitOps",
        "certifications": ["CKA", "CKAD"],
        "timeline": "Q2-Q3 2026"
    },
    "security": {
        "learning": "Advanced penetration testing & bug bounty",
        "certifications": ["OSCP"],
        "timeline": "Q4 2026"
    },
    "iti_engineering": {
        "petrol": {
            "learning": "Petroleum systems & industrial automation",
            "timeline": "Start: 2026-2027 (2 years)",
            "integration": "IoT, predictive maintenance, cloud analytics"
        },
        "electronics": {
            "learning": "Electronics & embedded systems",
            "timeline": "Start: 2026-2027 (2 years)",
            "integration": "Hardware automation, real-time systems, edge computing"
        }
    }
}

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🎯 GitHub Achievements & Statistics

📊 Profile Stats Summary

• Total Repositories: 50+
• Public Projects: 35+
• GitHub Followers: 1K+
• Total Commits: 10K+
• PRs Merged: 500+
• Code Review Contributions: 300+

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🎬 Recent Activity

• 🚀 Deployed ML model to production with 99.5% uptime
• 📝 Published 5+ technical blog posts on Medium
• 🏆 Ranked in top 1% on HackerRank (Python category)
• 🤝 Contributed to 15+ open-source projects
• 🎓 Mentored 10+ junior developers in backend architecture
• 🏭 Planning ITI enrollment for petroleum/electronics engineering (2026-2027)

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💻 Quick Code Examples

FastAPI REST Endpoint with Authentication

from fastapi import FastAPI, Depends, HTTPException
from fastapi.security import HTTPBearer, HTTPAuthCredentials
import jwt

app = FastAPI()
security = HTTPBearer()

async def verify_token(credentials: HTTPAuthCredentials = Depends(security)):
    token = credentials.credentials
    try:
        payload = jwt.decode(token, "SECRET_KEY", algorithms=["HS256"])
        user_id = payload.get("sub")
        if not user_id:
            raise HTTPException(status_code=401, detail="Invalid token")
        return user_id
    except jwt.InvalidTokenError:
        raise HTTPException(status_code=401, detail="Invalid token")

@app.get("/api/user/{user_id}")
async def get_user(user_id: str, current_user = Depends(verify_token)):
    if user_id != current_user:
        raise HTTPException(status_code=403, detail="Unauthorized")
    return {"user_id": user_id, "status": "active"}

ML Model Pipeline with Scikit-learn

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
import joblib

# Build pipeline
pipeline = Pipeline([
    ('scaler', StandardScaler()),
    ('classifier', RandomForestClassifier(n_estimators=100, random_state=42))
])

# Train model
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
pipeline.fit(X_train, y_train)

# Evaluate
accuracy = pipeline.score(X_test, y_test)
print(f"Accuracy: {accuracy:.4f}")

# Save model
joblib.dump(pipeline, 'model.pkl')

Docker Configuration

FROM python:3.11-slim

WORKDIR /app

COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY . .

EXPOSE 8000

CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

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🌟 Key Principles

"Code is poetry written in logic; architecture is the canvas."

Development Philosophy

• 🎯 Clean Code - Readable, maintainable, and well-documented
• 🏗️ Scalability First - Design systems to handle 10x growth
• 🔒 Security by Design - Never compromise on security
• 📊 Data-Driven Decisions - Metrics over guesses
• 🤝 Collaboration - Knowledge sharing and mentorship
• 🔧 Continuous Learning - Always expanding skill horizons across tech & engineering

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📞 Let's Connect!

Open for opportunities in:

• 🚀 Startup Co-founder roles
• 💼 Senior Backend Engineer positions
• 🤖 ML/AI Engineer positions
• 🏛️ Tech Lead & Architect roles
• 🏭 Industrial tech & automation projects
• ⚙️ IoT & embedded systems integration

📧 Best way to reach: kiranmondal5516@gmail.com

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Made with ❤️ by Kiran Mondal | Last Updated: June 2026

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