Software Engineer

Alexis Aguilar

Grand Prairie, TX 214-***-**** ************@*****.*** https://www.linkedin.com/in/aaguilar1892 https://github.com/aaguilar1892

EDUCATION

Johns Hopkins University, Baltimore, MD

Master of Science - Robotics and Autonomous Systems University of North Texas, Denton, TX GPA: 3.94

Bachelor of Science - Data Science

Bachelor of Science - Computer Science

TECHNICAL SKILLS

Data Analytics: Linear Regression, K-Mean, KNN, Neural Network, Exploratory Data Analysis, Machine Learning Deep Learning: Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) Software and Tools: SPSS, SAS, Tableau, PowerBI, Spark, TensorFlow, PyTorch, Hadoop, Terraform, RapidMiner, Pandas, Numpy, NodeJS, Bun, Angular, Spring Boot, React, Flutter, FastAPI, PWA, iOS, Android, Docker Databases: MongoDB, MySQL, PostgreSQL, Neo4j, Redis, Aurora, DocumentDB, DynamoDB, Supabase, Firebase Programming Languages: Python, R, C, C++, JavaScript, Rust, HTML/CSS, Java, SQL, Dart, Go, Bash Architectures and Project Management: Agile, DevOps, Jenkins, GitLab, Microservices, MVC, Layered, Client-Server Cloud Technologies: Amazon Web Services, Microsoft Azure, Akamai Linode, Google Cloud Platform EMPLOYMENT EXPERIENCE

Full Stack Software Engineer June 2021 - Present

PranceTech, Dallas, TX

● Engineered and launched web applications utilizing Python, JavaScript, Java, and SQL.

● Designed and deployed machine learning models that enhanced client operational efficiency by 30%, addressed challenges in finance, healthcare, and retail, and boosted client satisfaction ratings by 45%.

● Created and executed over 50 automation programs, cutting daily and repetitive task time by 40% and driving a 25% increase in team productivity.

● Architected and optimized a real-time data processing engine in C++ for back-end services, reducing data latency by 35%, improving system scalability, and integrating C++ modules into front-end APIs for seamless user experiences.

● Managed and maintained in-house Linux-based servers, applying load balancing and server optimization techniques that elevated uptime to 99.9% and slashed server response times by 20%. Data Science Intern January 2020 - May 2021

AT&T, Dallas, TX

● Engineered advanced predictive models using machine learning algorithms, optimized marketing strategies, and increased campaign ROI by 45% through targeted customer segmentation and personalized outreach.

● Collaborated with team leads to perform regression testing on products

● Created and compiled over 250 pipeline reports, reported monthly changes to front line supervisors

● Assisted in data cleaning, preprocessing, and exploratory data analysis.

● Constructed customization framework using Java and other programming languages

● Conducted user training sessions on data analysis tools and best practices SELECT PROJECTS

VoiceBridge - American ASL to Speech Web Application

● Developed backend infrastructure using Python, Flask, and OpenCV to process real-time webcam video streams for ASL recognition, integrating Mediapipe and TensorFlow models to translate sign language into text.

● Implemented text-to-speech functionality to convert translated ASL text into spoken English, enhancing accessibility for low-vision users.

● Designed and maintained CI/CD pipelines with GitHub Actions and Docker Compose to automate testing, building, and deployment workflows.

● Collaborated in an Agile Scrum team across three iterative sprints to deliver a fully functional web application. Food Deserts - Analysis on Food Deserts - Datathon Project- 2nd Place

● Utilized visualizations to uncover trends, identifying poverty rate and SNAP participation as critical predictors of food inaccessibility.

● Built and optimized Random Forest, XGBoost (achieving 87.4% accuracy), and Neural Network classifiers to predict food deserts effectively.

● Developed actionable, data-driven policy interventions, such as expanding SNAP benefits and enhancing grocery store subsidies, to improve food accessibility.

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