About
2.5+ years of experience developing and deploying ML solutions for AT&T. Skilled in building ML pipelines, ETL with PySpark, SQL, and Azure cloud's data engineering tech stack. Currently building a Multimodal RAG application for a large enterprise. Published researcher in deep learning.
Experience
Graduate Research Assistant·University of Florida
- ▹Led the development of Citrus-Ecoli-FluoroNet, achieving 90% accuracy in E.coli classification
- ▹Implemented MLOps practices with MLflow and Docker for reproducibility
- ▹Engineered explainable AI techniques with YOLOv8 Eigen-CAM
- ▹Designed MongoDB schema for 100GB+ metadata, optimizing retrieval by 25%
- ▹Implemented A/B testing for ML models evaluation
AI Software Developer Intern·GeoSpider
- ▹Developed GenAI-powered support system with FastAPI and LangChain
- ▹Implemented FAISS vector search with HNSW algorithm
- ▹Created real-time LLM API interface for improved user experience
Data Engineer·AT&T (with Accenture)
- ▹Built NLP pipelines for technician dispatch prediction
- ▹Developed ML ensemble model saving $2M annually
- ▹Optimized ETL workflows with Azure and PySpark
- ▹Improved data processing time by 30% with PySpark optimization
- ▹Migrated to Elasticsearch for enhanced log analytics
Skills
Machine Learning & GenAI
Cloud Platforms
Programming Languages and Tools
Projects
RAG Anything
Developed a RAG document chat application, leveraging LangChain, SQLDatabaseChain, OpenAI embeddings, and FAISS, implementing control flow for question-based LLM selection.
Idiomatically-Llama
Fine-tuned LLama2 7B model for idiom enhancement, built RAG pipeline with LangChain and CromaDB, achieved 0.91 ROUGE-L F1 score using 4-bit quantization.
Education
Master of Computer & Information Science
University of Florida
GPA: 3.5/4
Bachelor of Technology in Computer Science
GITAM University
GPA: 3.8/4