Data scientist and AI engineer with 5+ years of experience building NLP, computer vision, recommender, forecasting, and production AI systems for healthcare, risk, and operational analytics.
Data Scientist and AI Engineer with 5+ years of experience across fintech, travel, healthcare AI, and applied ML systems. I design end-to-end ML products from data pipelines to deployment, with a focus on measurable business impact, production reliability, and explainable decisioning. Currently pursuing an MS in Data Science at the University of Maryland while building multimodal, retrieval-based, and agentic AI products.
Python, SQL, PySpark, C/C++, JavaScript, MATLAB. Production coding for data pipelines, API backends, and analytics workflows.
LLMs, RAG systems, agentic AI, multimodal modeling, deep learning, machine learning, recommender systems, time-series forecasting, anomaly detection, NLP, and computer vision.
PyTorch, TensorFlow, Transformers, LangChain, LangGraph, FastAPI, OpenCV, Scikit-learn, Pandas, NumPy, Statsmodels, Streamlit, and model-serving toolchains.
PostgreSQL, Supabase, pgvector, Snowflake, Redshift, Spark, MySQL, MS SQL Server, Neo4j, and robust ETL/ELT data engineering pipelines.
AWS, GCP, Azure, Docker, Kubernetes, Terraform, GitHub Actions, CI/CD, MLOps, Vercel, and cloud deployment patterns for AI applications.
Tableau, Power BI, Matplotlib, Seaborn, statistical analysis, experiment evaluation, KPI storytelling, and decision-focused BI reporting.
Databricks, Dataiku, KNIME, Git, Jupyter, VS Code, Postman, Ollama, API development, experiment tracking, and reproducible project operations.
Executive communication, cross-functional collaboration, stakeholder translation, ownership mindset, problem decomposition, mentoring, and delivery under ambiguity.
Co-authored a peer-reviewed IEEE paper on using deep learning to improve reliable routing decisions in wireless ad hoc networks, connecting network-state modeling with more resilient path selection.
View publication5+ years of industry experience across healthcare AI, fintech risk analytics, travel operations, and production ML systems. Core strengths include end-to-end model lifecycle ownership, reproducible experimentation, cloud deployment, and business-facing communication.
Open to ML Engineer, Applied Scientist, and Data Scientist roles focused on LLM systems, multimodal AI, and production-grade intelligent platforms.