Building intelligent, data-driven and production-oriented software systems.
I am an AI/ML Engineer focused on building practical applications that combine:
Machine Learning + Generative AI + Data + Backend Engineering + Software Development
I enjoy taking an idea from:
Problem → Data → Intelligence → API → Database → Interface → Deployment
My work spans AI/ML, Generative AI, RAG systems, NLP, Data Science, Data Analytics, Backend APIs, and full-stack AI applications.
I am particularly interested in building systems where AI is not just a model or chatbot, but an integrated part of a complete software product.
|
Machine Learning Predictive Modeling NLP Explainable AI Deep Learning |
LLMs RAG AI Agents Embeddings Hybrid Retrieval |
FastAPI React REST APIs PostgreSQL Docker |
|
Data Science SQL EDA Feature Engineering Power BI |
Semantic Search Vector Search Reranking Knowledge Retrieval Grounding |
Docker Nginx CI/CD Cloud Deployment Production Systems |
| Area | Experience / Focus |
|---|---|
| 🤖 AI / ML | Machine Learning, Deep Learning, Predictive Modeling, NLP |
| 🧠 Generative AI | LLMs, RAG, AI Agents, Prompt Engineering |
| 🔎 Retrieval | Semantic Search, Hybrid Retrieval, Vector Search, Reranking |
| 📊 Data Science | Data Cleaning, EDA, Feature Engineering, Modeling |
| 📈 Data Analytics | SQL, Power BI, DAX, Power Query, Dashboards |
| ⚙️ Backend | FastAPI, Django, REST APIs, WebSockets |
| 💻 Software | Python, Java, JavaScript, TypeScript, React |
| 🗄️ Databases | PostgreSQL, MySQL, MongoDB, Redis, Vector Databases |
| 🚀 DevOps | Docker, Nginx, CI/CD, Cloud Deployment |
IntelliICU is a production-oriented healthcare AI platform designed around real-time ICU monitoring, machine learning-based risk prediction, explainable AI, RAG, and clinical decision-support workflows.
This project demonstrates the ability to combine AI + backend engineering + real-time systems + databases + frontend development into one complete application.
Python FastAPI React WebSockets XGBoost SHAP RAG PostgreSQL Docker
- Real-time ICU patient monitoring
- Machine learning-based clinical risk prediction
- Explainable AI using SHAP
- RAG-based knowledge workflows
- Clinical decision-support functionality
- Real-time communication using WebSockets
- FastAPI backend architecture
- PostgreSQL persistence
- React-based clinical interface
- Docker-based deployment
CLINICAL DATA
│
▼
DATA PROCESSING
│
┌──────────────┴──────────────┐
│ │
▼ ▼
RISK PREDICTION KNOWLEDGE LAYER
│ │
▼ ▼
XGBoost RAG
│ │
▼ ▼
SHAP KNOWLEDGE RETRIEVAL
│ │
└──────────────┬──────────────┘
│
▼
CLINICAL INTELLIGENCE
│
▼
FASTAPI BACKEND
│
┌────────────┴────────────┐
│ │
▼ ▼
PostgreSQL WebSockets
│ │
└────────────┬────────────┘
│
▼
REACT DASHBOARD
https://github.com/Sumeet2005/IntelliICU
An enterprise-oriented knowledge assistant focused on grounded LLM responses, document retrieval, hybrid search, citations, telemetry, and production-oriented architecture.
TypeScript LLMs RAG Hybrid Retrieval Vector Search Docker
- Retrieval-Augmented Generation
- Document ingestion and indexing
- Semantic retrieval
- Keyword retrieval
- Hybrid retrieval
- Reranking
- Grounded answers
- Citation-based responses
- Real-time telemetry
- Production-oriented architecture
- Docker deployment
Documents
│
▼
Document Ingestion
│
▼
Chunking
│
▼
Embeddings
│
▼
Vector Index
│
▼
User Query
│
▼
Query Processing
│
├───────────────┐
▼ ▼
Semantic Search Keyword Search
│ │
└───────┬───────┘
▼
Hybrid Retrieval
│
▼
Reranking
│
▼
Context Construction
│
▼
LLM
│
▼
Grounded Response
│
▼
Citations
https://github.com/Sumeet2005/AI-Knowledge-Chatbot
An AI-powered application combining React, FastAPI, LLM workflows, LangGraph, and PostgreSQL to process and manage pharmaceutical complaints.
React FastAPI LangGraph Groq PostgreSQL
- AI-powered complaint processing
- LLM integration
- LangGraph workflow orchestration
- Complaint classification
- Structured outputs
- FastAPI backend
- PostgreSQL persistence
- React frontend
Complaint │ ▼ Validation │ ▼ LLM Processing │ ▼ LangGraph Workflow │ ├──────────────► Analysis │ ├──────────────► Classification │ └──────────────► Structured Output │ ▼ PostgreSQL │ ▼ React Application
https://github.com/Sumeet2005/ai-pharmaceutical-complaint-system
An AI-powered resume platform combining resume analysis, ATS scoring, job matching, NLP processing, and blockchain-based certificate verification.
Python AI/ML NLP Solidity Web3
- Resume parsing
- NLP processing
- ATS scoring
- Job matching
- Automated resume insights
- Blockchain certificate verification
- Smart contract integration
Resume │ ▼ Resume Parsing │ ▼ NLP Processing │ ├──────────────► ATS Score │ ├──────────────► Job Matching │ └──────────────► Resume Insights │ ▼ Certificate Verification │ ▼ Blockchain │ ▼ Smart Contract
https://github.com/Sumeet2005/Smart_resume_analyzer_with_Blockchain_certification
A data science project focused on using historical data to develop predictive models for sales forecasting.
Python Pandas NumPy Scikit-learn Jupyter
- Data preprocessing
- Data cleaning
- Exploratory Data Analysis
- Feature engineering
- Visualization
- Predictive modeling
- Model evaluation
- Sales forecasting
Raw Data │ ▼ Data Cleaning │ ▼ EDA │ ▼ Feature Engineering │ ▼ Model Development │ ▼ Model Evaluation │ ▼ Prediction │ ▼ Sales Forecast
https://github.com/Sumeet2005/Sales-Prediction-using-Data-Science
An interactive healthcare analytics dashboard focused on transforming, analyzing, and visualizing healthcare data.
Power BI DAX Power Query Data Analytics
- Interactive dashboards
- Data transformation
- Power Query
- Data modeling
- DAX calculations
- Healthcare analytics
- Data visualization
- Business-oriented insights
Healthcare Data
│
▼
Power Query
│
▼
Data Transformation
│
▼
Data Modeling
│
▼
DAX
│
▼
Visualization
│
▼
Dashboard
│
▼
Business Insights
https://github.com/Sumeet2005/HealthCareDashboard-Using-Power-Bi
Worked on practical data science, machine learning, and NLP workflows using Python and Scikit-learn.
- Data preprocessing
- Data cleaning
- Exploratory Data Analysis
- Feature engineering
- Machine learning workflows
- NLP processing
- Model testing
- Model evaluation
- Performance analysis
Raw Data ↓ Cleaning ↓ EDA ↓ Feature Engineering ↓ ML / NLP ↓ Testing ↓ Evaluation
RAG Embeddings Vector Search Semantic Search Hybrid Retrieval Reranking Knowledge Retrieval Grounded Generation
S U M E E T S O N A R
│
┌─────────────────────────┼─────────────────────────┐
│ │ │
▼ ▼ ▼
AI / ML GEN AI DATA
│ │ │
ML Models LLMs Python
XGBoost RAG Pandas
NLP Agents NumPy
SHAP Search SQL
│ │ │
└─────────────────────────┼─────────────────────────┘
│
▼
BACKEND
│
┌────────────┼────────────┐
│ │ │
FastAPI REST WebSockets
│ │ │
└────────────┼────────────┘
│
▼
DATABASE
│
PostgreSQL • MySQL • MongoDB
│
▼
FRONTEND
│
React • TypeScript
│
▼
DEVOPS
│
Docker • Nginx • CI/CD
│
▼
PRODUCTION SYSTEM
I focus on building complete systems rather than isolated models.
REAL-WORLD PROBLEM
│
▼
DATA
│
▼
DATA PROCESSING
│
▼
┌───────────────────────┐
│ AI LAYER │
│ │
│ ML • LLM • RAG • NLP │
└───────────┬───────────┘
│
▼
BACKEND
│
▼
DATABASE
│
▼
FRONTEND
│
▼
TESTING
│
▼
DEPLOYMENT
This approach allows me to work across the entire application lifecycle:
Data → Intelligence → Engineering → Product → Deployment
- Classification
- Regression
- Predictive Modeling
- Feature Engineering
- Model Evaluation
- Explainable AI
- NLP
- Deep Learning
- Large Language Models
- Prompt Engineering
- RAG
- AI Agents
- Embeddings
- Vector Search
- Semantic Search
- Hybrid Retrieval
- Reranking
- Grounded Generation
- API-based inference
- Real-time systems
- Database integration
- Model explainability
- Retrieval pipelines
- Dockerized applications
- Monitoring and telemetry
RAW DATA │ ▼ CLEAN │ ▼ TRANSFORM │ ▼ ANALYZE │ ▼ MODEL │ ▼ VISUALIZE │ ▼ INSIGHT │ ▼ DECISION
Python Pandas NumPy SQL Power BI DAX Power Query Matplotlib Jupyter
I build backend services with an emphasis on:
- Clean architecture
- REST API design
- API integration
- Database persistence
- Real-time communication
- Modular services
- Validation
- Scalability
- Maintainability
- Deployment
CLIENT
│
▼
API GATEWAY
│
▼
FASTAPI / DJANGO
│
┌───────┼────────┐
│ │ │
▼ ▼ ▼
LOGIC AI/ML SERVICES
│ │ │
└───────┼────────┘
│
▼
DATABASE
│
▼
RESPONSE
CGPA: 7.63
- Artificial Intelligence
- Machine Learning
- Data Science
- Data Analytics
- Software Engineering
- Backend Development
- Generative AI
- Database Systems
My current learning direction is focused on moving from individual AI capabilities toward complete AI systems.
Generative AI
↓
Advanced RAG
↓
AI Agents
↓
LLM Application Architecture
↓
Backend System Design
↓
Production AI
↓
Cloud Deployment
- Advanced RAG architectures
- Agentic AI
- LLM application development
- AI system architecture
- Backend architecture
- Database design
- Docker
- CI/CD
- Cloud deployment
- System design
Data Science
│
▼
Machine Learning
│
▼
AI / ML Engineering
│
▼
Generative AI
│
▼
RAG Systems
│
▼
AI Agents
│
▼
Backend Engineering
│
▼
Full-Stack AI Applications
│
▼
Production AI Systems
| Project | AI/ML | GenAI | RAG | Backend | Database | Frontend | Analytics | Web3 |
|---|---|---|---|---|---|---|---|---|
| IntelliICU | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | — | — |
| AI Knowledge Chatbot | — | ✓ | ✓ | ✓ | ✓ | ✓ | — | — |
| Pharmaceutical Complaint System | — | ✓ | — | ✓ | ✓ | ✓ | — | — |
| Smart Resume Analyzer | ✓ | — | — | — | — | — | — | ✓ |
| Sales Prediction | ✓ | — | — | — | — | — | ✓ | — |
| Healthcare Dashboard | — | — | — | — | — | — | ✓ | — |
My GitHub work is primarily centered around:
| Category | Focus |
|---|---|
| 🤖 AI/ML | Machine Learning, NLP, Predictive Modeling |
| 🧠 GenAI | LLMs, RAG, Agents |
| 🔎 Retrieval | Semantic Search, Hybrid Retrieval |
| 📊 Data | Data Science, Analytics, Visualization |
| ⚙️ Backend | FastAPI, Django, APIs |
| 💻 Software | Python, Java, React, TypeScript |
| 🚀 Deployment | Docker, CI/CD, Cloud |
A model alone is not the final product.
The real value comes from connecting:
DATA ↓ INTELLIGENCE ↓ ENGINEERING ↓ APPLICATION ↓ USER ↓ IMPACT
- 🧩 Modular architecture
- 🧠 Meaningful AI integration
- 🔎 Explainability and grounding
- ⚙️ Maintainable code
- 🔐 Security-aware development
- 🧪 Testing and validation
- 📈 Continuous improvement
- 🚀 Deployment-focused engineering
My strongest projects combine multiple engineering disciplines instead of focusing on only one technology.
AI + ML + RAG + Real-Time + Backend + Database + Frontend
LLMs + RAG + Hybrid Retrieval + Search + Grounded Generation
LLMs + LangGraph + Backend + Database + Frontend
AI/ML + NLP + ATS + Blockchain
Data Processing + Analytics + Visualization + Predictive Modeling
CURRENT SKILLS
│
┌────────────┼────────────┐
│ │ │
AI GenAI DATA
│ │ │
└────────────┼────────────┘
│
▼
Advanced RAG
│
▼
AI Agents
│
▼
System Design
│
▼
Production AI
│
▼
Cloud Architecture
https://sumeet-portfolio-orcin.vercel.app
My portfolio provides a broader view of my:
- Projects
- Technical skills
- Experience
- Development journey
- AI/ML work
- Software engineering work
I am interested in opportunities involving:
- AI/ML Engineering
- Generative AI
- Machine Learning
- Data Science
- Data Analytics
- Backend Engineering
- Python Development
- AI Application Development
- Software Engineering
AI / ML + Generative AI + LLMs / RAG + Data + Backend + Software Engineering + Production Deployment
🌐 Portfolio
💻 GitHub