AI Product Engineer with hands-on experience building LLM-powered applications, production RAG systems, and agentic AI workflows.
Currently working as an AI Engineer Intern at Rubixe AI Solution Company, where I transform complex AI concepts into scalable, production-ready features.
Specialization:
- Building AI products with LLMs (LangChain, LangGraph, Google Gemini)
- Designing & implementing RAG systems for real-world use cases
- Creating agentic AI workflows and autonomous agents
- Mobile app development with React Native & modern backends
Career Goal: AI Product Engineer | Applied ML Specialist
- Built Production AI Systems: Designed and deployed RAG pipelines for intelligent document processing
- Mobile AI Integration: Developed AI-powered Nutri Ninja app with real-time inference and local caching
- End-to-End AI Products: Shipped full-stack AI applications from design to production
- LLM Engineering: Expertise in prompt optimization, retrieval augmentation, and agentic workflows
An intelligent nutrition assistant that scans food barcodes and provides instant health insights with AI-powered diet advice.
Features:
- Real-time barcode recognition with ML inference
- AI-powered diet chatbot (LLM integration)
- Health scoring algorithm with ingredient analysis
- Local data storage for privacy
- Cross-platform support (React Native + Expo)
Tech Stack: React Native | TypeScript | FastAPI | Python | AI/ML models | Firebase
Impact: Processed nutrition data for 1000+ food items with personalized recommendations
Repository: Nutri-Ninja-5.0
Production-ready conversational AI system leveraging LangChain for orchestration and Google Gemini for intelligent responses.
Features:
- Multi-turn conversation with context management
- LLM-powered responses with real-time streaming
- Memory management for context retention
- Environment-based secure configuration
- Error handling & retry logic
- Extensible architecture for production systems
Tech Stack: Python | LangChain | Google Gemini API | Python-dotenv
Use Case: Foundation for customer support systems, internal Q&A platforms, educational assistants
Repository: Agent-Chat-Bot
Computer vision system for real-time facial emotion detection with music-based mood responses and analytics dashboards.
Features:
- Live emotion classification (Happy, Sad, Angry, Neutral, etc.)
- Adaptive music player based on detected emotions
- Web & PyQt dashboards for emotion analytics
- CSV/SQLite logging for emotion tracking
- Educational & demo use cases
Tech Stack: Python | DeepFace | OpenCV | PyQt | Flask | SQLite
Applications: Behavioral analysis, educational tools, interactive installations
Repository: Emotion-Detection
Computer vision model for detecting and classifying furniture items in images using state-of-the-art YOLO architecture.
Tech Stack: YOLOv8 | Python | OpenCV | Jupyter
Repository: YOLOv8_Furniture_Object_Detection
Full-featured mobile development showcase with user authentication, social login integration, and e-commerce workflows.
Features:
- User signup & secure authentication
- Facebook OAuth integration
- E-commerce product browsing & checkout UI
- Cross-platform mobile development patterns
Tech Stack: React Native | Expo | TypeScript
Repository: React_Native_Practice-1.0
| Project | Topics | Repository |
|---|---|---|
| Python Fundamentals & Games | Core Python, OOP, Game Development | Python-Code-Fundamentals-and-Games |
| Data Science Stack | NumPy, Pandas, Matplotlib fundamentals | Pandas_Practice | Numpy_Practice | Matplotlib_Practice_and_Bonus |
| Machine Learning | ML algorithms, model training, evaluation | Machine-Learning-Project |
| Computer Vision | MobileNet Transfer Learning for mobile deployment | Computer-Vision-MobileNet_Transfer-Learning |
| DSA Practice | Data Structures & Algorithms problem solving | DSA_Practice_Problem |
- Advanced RAG architectures (Multi-agent retrieval, Hybrid search strategies)
- LLM fine-tuning and advanced prompt engineering
- Production-grade AI systems with FastAPI + Docker
- Scaling AI applications with cloud platforms (GCP, AWS)
- Multi-agent AI systems and orchestration patterns
I am actively interested in discussing AI engineering, LLM applications, and innovative product development opportunities.
Open to: Full-time AI Engineer | ML Engineer | AI Product Engineer roles
Contact:
- Email: dewanganjitendra725@gmail.com
- LinkedIn: linkedin.com/in/jitendradewangan/
- GitHub: github.com/JitendraDew009
Learn → Build → Ship → Iterate → Repeat
Core Principles:
- Writing clean, maintainable, and well-documented code
- Building features with measurable real-world impact
- Continuous learning and technical exploration
- Contributing to open-source communities
- Shipping production-ready solutions
If my projects have been helpful, please consider starring them.
Last updated: September 2024