AIthatearnsitsplace.

Building autonomous agent workflows and production RAG systems, with responsible-AI governance and statistical rigor across the model lifecycle.

• ▸Architected autonomous agent workflows using LLMs, tool-use, and decision logic, cutting manual effort by 70-85% across internal operations.
• ▸Engineered production RAG systems with embeddings and vector databases (OpenSearch, Pinecone), boosting retrieval relevance by 40%.
• ▸Applied disparate-impact analysis and statistical bias assessment to review model outputs for fairness risk; partnered with compliance and legal on risk-mitigation decisions.
• ▸Designed and validated predictive ML models end-to-end: feature engineering, hypothesis testing, and lifecycle documentation aligned with responsible-AI governance.
• ▸Ran descriptive, predictive, and prescriptive analyses on large datasets with Python and PySpark on Azure Databricks; built Power BI dashboards to communicate results to technical and non-technical leadership.
• ▸Deployed serverless components on AWS Lambda, SQS, and Step Functions; packaged with Docker; provisioned via Terraform.

Built LLM-assisted analytics and multi-cloud data architecture supporting 20+ research projects.

• ▸Developed LLM-assisted analytics for summarization, classification, and context extraction using RAG and prompt strategies, shortening analysis turnaround by 35%.
• ▸Designed multi-cloud architecture with Snowflake, BigQuery, and NoSQL supporting 20+ projects with standardized schemas.
• ▸Trained and evaluated ML pipelines with TensorFlow, PyTorch, clustering, and statistical methods, elevating forecasting quality by 35%.
• ▸Streamlined orchestration using Airflow, Spark, and Databricks, accelerating pipeline runtimes by 55% while sustaining 99.9% reliability.

Designed ETL pipelines, predictive models, and executive BI dashboards for enterprise reporting.

• ▸Designed ETL pipelines and analytics data models using Databricks, Spark, and warehouses (Redshift, Snowflake, BigQuery), decreasing reporting latency by 55%.
• ▸Applied predictive modeling and KPI forecasting using Python and TensorFlow, increasing planning accuracy by 20%.
• ▸Produced executive dashboards in Power BI and standardized KPI definitions, increasing stakeholder adoption by 30%.