About Me
I am an AI/ML Engineer and Data Engineer with over 3 years of experience building production machine learning systems and scalable data pipelines that process more than 2 GB of data daily. My expertise lies in Retrieval-Augmented Generation (RAG), MLOps, and real-time streaming architectures. I have successfully reduced data latency by 98%, improved query performance by 97%, and deployed ML models achieving 90% accuracy for predictive analytics in manufacturing operations.
My technical skills include working with ML/AI frameworks such as PyTorch and TensorFlow, integrating large language models like OpenAI and LangChain, and managing cloud platforms including AWS and Azure. I am proficient in building ETL/ELT pipelines, real-time streaming with Kafka, and designing dimensional data models for OLAP warehouses.
I am a strong communicator with a background in teaching, which enables me to translate complex technical concepts effectively for stakeholders and foster cross-functional collaboration. I have experience creating internal web tools and visualizations to improve operational efficiency and developer satisfaction.
Throughout my career, I have worked on automating data workflows using AI-driven solutions and integrating CI/CD pipelines to enhance deployment reliability. I am passionate about leveraging data engineering and machine learning to solve real-world problems and am open to remote opportunities in AI/ML Engineering and Data Engineering roles.
I am fluent in English and have limited working proficiency in Spanish. I continuously seek to expand my knowledge through certifications and hands-on projects, aiming to deliver impactful data-driven solutions.
Skills
PythonSQLData AnalysisJavaScriptReactDockerTroubleshootingKafkaTensorFlowAzure DevOpsTeachingAWS S3
Experience
Architected real-time streaming integration using Azure Functions and Kafka, processing 2+ GB of manufacturing data daily and reducing data availability lag from 4 hours to 2 minutes (98% latency reduction). Optimized Azure Cosmos DB queries and indexing strategies, accelerating report runtimes from 5 minutes to 10 seconds (97% improvement). Engineered ETL pipelines using Python, SQL, and AWS Lambda to ingest manufacturing data into Snowflake, maintaining 99.9%+ pipeline availability through automated monitoring and proactive failure resolution. Designed dimensional data models in MS SQL, MariaDB, and PostgreSQL for centralized OLAP warehouses. Built CI/CD workflows in Azure DevOps reducing deployment failures by 80%. Created internal web tooling using React and Power BI visualizations, reducing time-to-action by 40%. Integrated AI-driven automation using LLM APIs to improve task efficiency by 30%. Collaborated with product managers and BI stakeholders to define data product requirements and establish data governance.
Remotely provisioned and troubleshot Residential ONTs using various query tools. Liaised issue escalations between stakeholders. Improved communication between Account Managers, Customer Service, and Tier 2 teams. Created data visualizations webpage to provide insights on peer performance, leading to a 25% increase in daily solved tickets.
Assisted instructor in leading a 24-week Data Analysis and Data Visualization Bootcamp in partnership with Penn State University. Provided tutoring and support on Python, Excel, Pandas, and other Data Science tools.
Managed data integration into CRM systems including data quality checks, resulting in a 60% increase in lead generation. Ran usage reports on web services to promote adaptation or design cost-efficient operations saving thousands of dollars. Troubleshot issues and trained employees on best practices.
Completed 650+ hours in Data Science/Machine Learning Career Bootcamp. Developed a capstone project creating a predictive model for financial complaints involving data cleaning, model training, comparison, and API design hosted via Docker.
Created an engaging classroom environment. Designed a Geometry course using test/quiz score metrics to generate alerts for irregular patterns, achieving a 100% pass rate.
Part-time Teacher Assistant for Physics Labs. Trained college students in data analysis and deductive reasoning to prove physical laws. Updated grading rubrics based on student performance analysis.
Part-time Teacher Assistant for intro level physics labs using Investigative Science Learning Environment (ISLE) techniques. Worked with researchers to provide feedback and metrics on student performance and interest.
Education
Bachelor of Science in Physics
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