class Benhur:
"""Building intelligent agentic systems at enterprise scale."""
role = "Associate Data Scientist"
focus = "AI Engineering & Agentic Workflows"
stack = ["Agents", "RAG", "LLMs", "Kubernetes", "OpenShift", "MCP"]
ships = "Production-grade AI agents and autonomous systems"ai_and_ml:
frameworks: [LangChain, LangGraph, HuggingFace, Scikit-learn]
techniques: [RAG, Agentic Workflows, Predictive Modeling, Fine-tuning]
agents: [Deep Agent, React Agent, Autonomous Agentic Flows]
protocols: [MCP]
llms: [Claude, GPT, Gemini, Llama]
evaluation: [Custom benchmarks, Agentic-specific metrics, Model performance]
infrastructure:
orchestration: [Kubernetes, OpenShift, Docker]
cloud: [AWS]
ci_cd: [GitHub Actions, Jira]
languages: [Python, Java]
databases: [PostgreSQL, SQL Server]| Project | What it does | Why it matters |
|---|---|---|
| airline-operational-risk | AirOps Risk Predictor using RAG & ML | Demonstrates real-world application of RAG for operational risk mitigation |
| Sql-data-warehouse-project | End-to-end SQL Data Warehouse (Bronze → Silver → Gold) | Showcases strong data engineering and architecture foundations |
| credit-card-fraud-detection | Machine learning-based fraud detection on imbalanced data | Proves ability to handle complex datasets with XGBoost & PR-AUC evaluation |
| house-price-prediction-regression | Predictive modeling using Linear, Ridge, and Lasso regression | Highlights fundamental proficiency in classical regression techniques |
| Customer-Churn | End-to-end predictive analysis for customer churn | Applies classification modeling to solve a core business problem |
| used-car-price-estimator | Used Car Price Estimator | Practical application of end-to-end ML pipeline development |