I'm a curious and driven Data Scientist | AI/ML Engineer who enjoys figuring out the "why" behind anything and everything, with a Data Science background, focused on building practical, production-ready ML/AI systems and modern cloud-native applications.

I enjoy turning messy data, legacy workflows, and vague business problems into clean Python pipelines, deployable ML services, and useful AI-powered tools. I'm especially interested in the intersection of Machine Learning, Generative AI, and software engineering — where models actually ship and create real impact. My philosophy: understand the why behind a model, not just the how.

My recent work focuses on modernizing legacy analytical systems and building end-to-end AI/ML solutions. While most of this work lives in private company repositories, it includes:

• 🏗️ Designing and maintaining scalable Python pipelines for data processing and ML workflows
• 🔄 Re-engineering legacy statistical projects (SPSS/R) into modern, maintainable Python systems
• 🤖 Building and deploying ML models for real business use cases — forecasting, imputation, TV viewing prediction, and classification
• 🚀 Deploying containerized applications on AWS (Lambda, ECS/Fargate, S3, ALB) with CI/CD pipelines
• 💬 Prototyping RAG-based chatbot systems using modern LLM stacks (embeddings, vector search, prompt engineering)
• 🧠 Creating internal AI tools for database exploration, automation, and analytics support

• RAG-based Chatbot Systems: End-to-end chatbot using LangChain, embeddings, RAG and vector search for contextual document Q&A and workflow automation
• Production ML Models: Profitability forecasting using Scikit-learn and PyTorch, TV viewing prediction systems, and data imputation pipelines
• Python Automation: Automated data workflows using Python scripting and job scheduling for routine tasks
• Box Office Analytics: Movie and TV show analysis incorporating API integration and web scraping
• Medical AI: CNN-based Chest X-ray image classification with deployment focus
• Continuous Learning: Expanding knowledge in Cloud Technologies, Machine Learning, MLOps, and Generative AI

• 🚀 Building ML systems that actually ship and create impact
• 📊 Understanding the "why" behind it all — defining problems clearly and turning complex data into actionable insights
• ⚡ Making AI tools usable in real workflows, not just notebooks
• ⚙️ Automating repetitive tasks to focus on what matters
• 🎸 Balancing tech with music, fitness, and too many movies, series, and games
• 🤝 Always open to collaboration, fun side projects, or chatting tech!

• 🔨 Building production-ready ML pipelines and deploying them on AWS/Azure
• 💬 Developing RAG-based chatbot systems with LangChain and vector databases
• 📦 Modernizing legacy statistical projects (SPSS/R) into scalable Python systems
• 🤖 Creating ML models for forecasting, imputation, and classification tasks
• 🧪 Experimenting with semantic search, embeddings, and prompt engineering
• 📚 Learning MLOps best practices and advanced RAG architectures
• ☁️ Exploring cloud-native ML deployments and agentic AI systems
• 🔧 Just enough DevOps to be dangerous 😄