🚀 AI Engineer | ML Systems Builder | Generative AI Developer

Building scalable AI systems from data → model → production

• 🎓 B.Sc. Information Technology
• 📍 India (Madurai / Chennai) — Open to Relocation
• 💡 Focused on AI Engineering, Machine Learning Systems, and Generative AI Applications
• ⚙️ Experienced in building end-to-end ML pipelines, APIs, and AI-driven solutions
• 🏎️ Strong interest in Formula 1 data analytics & performance modeling

• Design and build production-ready ML systems
• Develop AI-powered applications using LLMs
• Convert data science workflows into scalable APIs
• Work with real-world datasets to solve business problems

• Built an end-to-end fraud detection pipeline using Random Forest, XGBoost
• Implemented:
  • Data preprocessing & feature engineering
  • Handling imbalanced datasets
  • Model evaluation using F1-score, ROC-AUC
• Designed for real-world financial risk detection use cases
• 🔗 https://github.com/ROCKYBH7/fraud-detection-app

• Developed a multi-stage AI agent (11-step workflow) using LangGraph
• Features:
  • Stateful conversation flow
  • Escalation handling logic
  • Tool/API integration (MCP routing)
• Demonstrates real-time AI automation system design
• 🔗 https://github.com/ROCKYBH7/langgraph-customer-support

• Built a text classification system using TF-IDF + Naive Bayes
• Achieved high accuracy with optimized preprocessing
• Integrated with Streamlit for real-time predictions
• Covers:
  • NLP pipeline
  • Visualization & insights
• 🔗 https://github.com/ROCKYBH7/sms-spam-detector

• Developed regression models for price prediction
• Includes:
  • Data cleaning & feature engineering
  • Outlier handling
  • Model evaluation using RMSE & R²
• Focused on practical ML pipeline design

• Performed deep exploratory data analysis
• Identified:
  • Fraud patterns
  • Feature correlations
  • Business insights from transaction data
• 🔗 https://github.com/ROCKYBH7/Fraud_Detection_EDA

• Scikit-learn, XGBoost, TensorFlow, PyTorch
• NLP, Feature Engineering, Model Evaluation

• LangChain, LangGraph
• LLM Applications, Prompt Engineering

• Python, FastAPI (API development)
• Modular architecture, pipeline design

• SQL, PySpark
• Data pipelines, ETL workflows

• Git, GitHub Actions
• Streamlit, Docker (learning)
• Azure / IBM Cloud (basics)

• Building AI applications with LLMs (RAG, Agents)
• Improving ML system design & deployment skills
• Transitioning into production-grade AI engineering

• AI / ML system development
• Generative AI applications
• Real-world problem-solving projects

• 📧 balajirh.ds@gmail.com
• 💼 https://www.linkedin.com/in/balaji-r-h-a81107298
• 🐙 https://github.com/ROCKYBH7

I focus on building AI systems that work in production — not just models in notebooks.