Senior AI Engineer at CAPITAL ONE (2025-02 – Present)
- Built production AI systems with TensorFlow and SQL that accelerated loan approvals and improved credit risk assessments.
- Developed LLM and RAG applications using embeddings and vector search to automate financial document analysis, accelerate insight extraction, and reduce review time.
- Built scalable AI services with Python, AWS, Kubernetes, and MLOps, enabling reliable, near-zero-downtime production deployments.
- Improved risk prediction accuracy by 15% through feature engineering and model optimization.
- Reduced manual review effort by 30% by automating financial analysis workflows.
- Partnered with data scientists and engineers to productionize AI solutions, improving reliability and scalability through model monitoring.
Senior Machine Learning Engineer at CAPITAL ONE (2022-08 – 2025-02)
- Developed and deployed PyTorch ML models for credit risk, lending decisions, and customer analytics, improving risk assessment accuracy.
- Built Python, SQL, Spark, and Snowflake pipelines processing millions of financial records daily, improving data availability for analytics.
- Boosted model performance through feature engineering, rigorous experimentation, and validation using PyTorch and Snowflake.
- Deployed and monitored production models using Docker and CI/CD, achieving 99.9% availability.
- Reduced inference latency by 35% through model and serving optimization.
- Automated model training and retraining with CI/CD, accelerating production updates and improving deployment efficiency.
Machine Learning Engineer at SCALE AI (2018-02 – 2022-08)
- Developed ML and NLP solutions that improved enterprise document-search accuracy and efficiency.
- Built Python, Spark, and Airflow pipelines, cutting data preparation time by 50% and enabling daily model retraining.
- Deployed containerized ML models with Docker and Kubernetes on Azure, delivering scalable, high-availability inference at 10,000+ requests per second.
- Automated model delivery through CI/CD, cutting deployment time from hours to minutes and improving deployment reliability.
- Increased production reliability by 30% and reduced model latency through expanded pytest coverage, Prometheus monitoring, Grafana dashboards, and performance tuning.
Data Scientist at ACTIVTRAK (2015-11 – 2017-12)
- Developed Python and TensorFlow predictive models that increased forecast accuracy and enabled faster, data-driven decisions.
- Built and deployed analytics and ML solutions that accelerated analysis, improved decision-making, and delivered measurable business impact.
- Built scalable Snowflake feature pipelines with Transformers, improving ML data quality and accelerating model development.
- Automated weekly reporting with Airflow and Python, cutting manual work by 80% and improving data quality through standardized validation checks.
- Integrated ML models into Java applications, enabling real-time predictions and improving application capabilities.
Data Analyst at ACTIVTRAK (2013-08 – 2015-11)
- Built and optimized SQL datasets, dashboards, and reports, enabling faster, data-driven business decisions.
- Improved data quality by automating validation and data processing in Azure Data Factory, reducing errors and enabling reliable analytics.
- Prepared and optimized clean, structured Snowflake datasets, accelerating analytics and machine learning workflows.
