AI/ML Engineer · Machine Learning · Deep Learning · Generative AI · MLOps

Building end-to-end AI systems that move from data → models → APIs → applications

I'm an AI/ML Engineer focused on building practical, production-oriented machine learning and Generative AI applications.

I work across the complete AI development lifecycle — from data preprocessing and model experimentation to deployment, APIs, experiment tracking, and RAG-based applications.

Currently focused on strengthening my skills across Machine Learning → Deep Learning → Generative AI → MLOps/LLMOps.

• 🔭 Building end-to-end ML, DL and GenAI projects
• 🤖 Working with Machine Learning, Deep Learning, NLP, Computer Vision and RAG
• 🧩 Building production-style pipelines using MLflow, DVC, Docker, FastAPI and Streamlit
• 🌱 Learning LLM fine-tuning, Hugging Face, Agentic AI and LLMOps
• 🎯 Open to AI/ML Engineer, Machine Learning Engineer, Applied AI and Generative AI internship opportunities

End-to-end machine learning pipeline for bank marketing prediction

Repository

Highlights

• Complete ML workflow from preprocessing to deployment
• Compared multiple classification algorithms
• Hyperparameter tuning with RandomizedSearchCV
• Addressed class imbalance using SMOTE
• Experiment tracking with MLflow
• Model persistence with Joblib
• REST API using FastAPI
• Interactive application using Streamlit

Stack: Python · Pandas · NumPy · Scikit-learn · XGBoost · SMOTE · MLflow · FastAPI · Streamlit

Privacy-focused Retrieval-Augmented Generation application for medical documents

Repository

Highlights

• Upload medical PDFs and create a real-time knowledge base
• Retrieval using FAISS
• Responses grounded in uploaded documents
• Privacy-focused document workflow
• Interactive Streamlit interface
• Secure API key handling with environment variables

Stack: Python · RAG · LLMs · LangChain · FAISS · Streamlit · PDF Processing

Deep learning computer vision system for industrial defect detection

Highlights

• Custom ResNet-50 architecture built with TensorFlow/Keras
• Image classification pipeline for industrial inspection
• MVTec AD dataset
• Training and experiment tracking with MLflow
• Model deployment architecture with FastAPI + Streamlit
• Designed with an MLOps-oriented workflow

Stack: Python · TensorFlow · Keras · Computer Vision · OpenCV · MLflow · FastAPI · Streamlit

Areas: RAG · Vector Search · Embeddings · LLM Applications · Prompt Engineering · NLP · Agentic AI

Focus: ML Pipelines · Experiment Tracking · Data Versioning · Model Deployment · REST APIs · Containerization

• Advanced RAG architectures
• Hybrid and multimodal RAG
• LLM fine-tuning with Hugging Face
• Agentic AI and multi-agent systems
• MLOps and LLMOps
• CI/CD for machine learning applications
• Production deployment of AI systems

AI/ML Engineer Machine Learning Engineer Applied AI Engineer Generative AI Engineer Computer Vision / NLP Engineer MLOps / LLMOps

I'm interested in collaborating on machine learning, Generative AI, RAG, computer vision and production AI projects.

Building AI systems one project at a time 🚀