AI/ML Engineer | Full-Stack Developer | Cloud & MLOps Enthusiast
🎓 MS in Computer Science @ CSUDH (Graduating May 2025)
💡 Passionate about building AI-powered systems, scalable ML pipelines, and intelligent web apps
🌍 Exploring AI for supply chain optimization, computer vision, and RAG-based systems
Languages: Python, Java, Scala, JavaScript
ML/DL: PyTorch, TensorFlow, Scikit-learn, XGBoost, LightGBM
MLOps & Data: Docker, Kubernetes, Ray, MLflow, DVC, Airflow
Backend: FastAPI, Django, Flask, Node.js
Frontend: React, Flutter, Bootstrap
Cloud: AWS (SageMaker, EC2, Lambda), GCP (Vertex AI), Azure
Optimizing inventory and supply chain forecasting using Walmart’s M5 dataset.
- Compared ML models: Linear Regression, Random Forest, XGBoost, LightGBM, Ridge, SARIMA
- Metrics: RMSE, MAPE, R², inventory turnover analysis
- Tools: Python, Scikit-learn, Pandas, Matplotlib
- Impact: Reduced stockouts & improved demand planning accuracy
Retrieval-Augmented Generation chatbot with knowledge grounding.
- Built with LangChain, OpenAI, ChromaDB
- UI in Streamlit with custom theme
- Features: semantic search, metadata filtering, context-aware answers
- Dockerized for easy deployment
Computer vision model to classify German traffic signs using transfer learning.
- Used TensorFlow + Keras (VGG16)
- Fine-tuned layers for improved accuracy
- Achieved >95% test accuracy
- Visualization: Grad-CAM heatmaps
End-to-end ML pipeline for customer insights & sales prediction.
- Segmentation: RFM + KMeans
- Forecasting: SARIMA, Prophet, LSTM
- Deployment-ready dashboards
- 🔭 Working on: Supply Chain Optimization with MILP + ML
- 📚 Learning: Advanced MLOps (Ray, Vertex AI)
- 🤝 Open to: Collaborations in AI/ML & full-stack development
- Portfolio Website
- 📧 Email: [email protected]
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