Work Experience
Merrill Lynch Wealth Management
Remote, May 2018 - May 2024
Senior ML Engineer
- Led the development of AI chatbot using PyTorch, LangChain, and Rasa to provide real-time financial advice and account management assistance; reduced customer churn by 20% by ensuring 24/7 service availability and instant query resolution.
- Developed machine learning models utilizing Transformers, LLMs to analyze market trends and predict client investment behaviors; resulted in a 30% revenue growth by enabling advisors to provide timely and informed recommendations.
- Designed a clustering algorithm using PyTorch, Pandas to segment clients based on demographics, investment preferences, and behaviors, enabling more personalized communication and service.
- Created a Monte Carlo Simulation-based risk assessment model that processed 1,000+ investment scenarios, equipping advisors with the necessary insights to optimize client portfolio strategies and improve risk management.
- Optimized Compliance Monitoring models to oversee transactions for regulatory compliance and detect potential fraudulent activities by implementing RAG, resulting in a 20% improvement in fraud detection accuracy.
- Architected a scalable machine learning infrastructure on AWS leveraging SageMaker, Kubernetes, Glue and MLOps, resulting in deployment efficiency and model performance.
- Determined the optimal number of pods in Kubernets by monitoring the system performance with Amazon CloudWatch, improved the overall system efficiency by 15%.
- Mentored 3 junior engineers in advanced model development and efficient version control practices, boosting team productivity by 25%.
HEB
San Antonio, TX, Oct 2014 - May 2018
Senior ML Engineer
- Engineered HEB Chatbot using Tensorflow, NLTK and Spacy to deliver personalized food recommendations based on real-time sentiment analysis, enhancing customer engagement and satisfaction by 30%.
- Implemented a machine learning model using TensorFlow and XGBoost to analyze customer movement patterns; enhanced retail layout strategies, leading to an impressive 10% revenue growth within six months of implementation.
- Designed a comprehensive pricing algorithm to recommend optimal pricing strategies based on analytics of historical sales data and market trends.
- Created an end-to-end machine learning pipeline on GCP with integrated tools such as Apache Kafka and MLflow; empowered analytics teams to derive insights from 500,000+ data entries seamlessly, transforming data-driven decision-making.
- Spearheaded integration testing for machine learning models using PyTest, implementing automated test cases to ensure seamless interoperability within production environments, resulting in a 40% reduction in production errors within three months.
- Collaborated with 3+ cross-functional teams, including data scientists, software engineers, and product managers to drive innovative solutions and ensure alignment on project goals and deliverables.
Technical Transportation
Southlake, TX, Jul 2013 – Oct 2014
Machine Learning Engineer
- Engineered a real-time traffic analysis application utilizing Google Maps API and machine learning algorithms; enhanced navigation efficiency, leading to reduced travel times by 25% for 5,000 daily users.
- Built a driver behavior classification model using scikit-learn and XGBoost to classify driver behavior based on speed, braking patterns, acceleration, and fuel consumption, enhancing safety measures and optimize fuel efficiency by 20%.
- Developed real-time routing model that adjust based on current traffic conditions, accidents and road closures with Tensorflow, enhancing transport efficiency be 15%.
- Deployed GIT for version control, automated build processes with Jenkins, and integrated JIRA for commit monitoring.