Martin Alvarez

Recommendation Engine & Generative AI — LegalTech Platform: • Architected and deployed a Generative AI pipeline using AWS Bedrock and LangChain, implementing RAG to integrate Thomson Reuters’ product metadata. This automated the generation of hyper-personalized marketing messages, directly impacting customer retention and upsell metrics. • Designed a real-time recommendation system on AWS SageMaker using XGBoost for rank-ordering legal resources. Integrated collaborative filtering with SageMaker Endpoint

CU Station — Credit Union Loan Matching Platform: • Built the platform’s primary data source by designing an AI-powered pipeline: web scraping with Python, HTML-to-vector embeddings, storage in a vector database, and a RAG system in LangChain to auto-detect loan products and interest rates. • Automated ingestion of reports from the U.S. National Credit Union Administration (NCUA) via an ETL pipeline, storing them in a relational database to deliver real-time updates on credit unions’ public information.

• Led the end-to-end development of a churn mitigation engine on Vertex AI, implementing automated retraining loops and Vertex AI Model Monitoring to detect data drift. Leveraged SQL-based feature engineering and managed model lifecycle, achieving an 8% decrease in churn rate. • Designed custom retention strategies by using statistical techniques such as factor analysis to identify key factors driving customer behavior, resulting in an 8% decrease in customer churn rate and showcasing the ability to use adv

• Eliminated all manual data-entry effort for a six-person sales team by developing a Python ETL to auto-extract KPIs from their CRM and populate a dynamic Power BI dashboard, driving an estimated 12 % sales increase and enhanced job satisfaction within three months. • Helped companies in the logistics industry track employee performance through the design of business relevant KPIs. • Day-to-day operations included developing dashboards for US-based customers in the logistics industry to visualize key busin

• Improved On-Time Delivery Rate from 89% to 95% by developing a data pipeline in Python that provided high-frequency visibility and alerts of freight status. • Develop ETL pipelines to receive and store transactional data from the logistics industry. • Submit periodical reports based on operational results for transportation carriers.

• Maintained Customer Satisfaction Score (CSAT) above 80% through effective monitoring of the operation. • Executed complex SQL queries for customer data retrieval and database population. • Developed PowerBI dashboards to centralize key customer service KPIs, such as Average Response Time, Customer Satisfaction Score (CSAT) and First Contact Resolution (FCR). resulting in an improvement in service efficiency. • Analyzed trends in customer queries and warranty claims data, using these insights to inform imp