AI/ML Engineer with 2.5+ years of expertise in machine learning, deep learning, and data analytics. Skilled in building scalable models, data pipelines, and cloud-based deployments.
DOWNLOAD PDF ↓Hardened observability across a multi-module Spring Boot codebase with SLF4J structured logging. Contributed to the Table Guard Web API exposing JWT-secured REST resources for casino surveillance. Supported ETL + rule-engine pipelines ingesting and transforming table game data for real-time risk signals.
Designed an AI-powered multi-agent tutoring platform that improved learner outcomes by 30% and lifted engagement by 35%. Architected a scalable RAG pipeline with recursive chunking and metadata-aware vector search, cutting topic confusion by 42%. Built intelligent document processing with 98% extraction accuracy and 90% reduction in API costs.
Led development of a Python-based time series forecasting tool using ARIMA, SARIMA, SARIMAX, VARMAX, and RNN models, reducing exploratory time by 70%. Automated hyperparameter tuning across 50+ iterations per dataset, cutting delivery from 3 weeks to 4 days. Enhanced model accuracy by 12% with iterative feature engineering.
Predicted sales using ARIMA and Prophet models, reducing overstock by 12% and stockouts by 9%. Improved product discovery with semantic embedding search, boosting session duration by 30%. Developed K-Means/DBSCAN customer segments for personalized campaigns that raised repeat purchase rates by 18%.
Large-scale retrieval-augmented recommendation engine over the 8GB+ Yelp dataset. Semantic retrieval paired with LLM-based generation delivers context-aware dining suggestions across 10K+ businesses with low-latency querying.
End-to-end ML classification pipeline with advanced feature engineering and hyperparameter tuning. SMOTE for class imbalance and Boruta for feature selection raised predictive performance by ~18%. Dimensionality reduction cut training time by ~30% without accuracy loss.