Building reliable data pipelines and production-ready ML systems.

Data Engineer and Machine Learning Engineer who turns raw data into deployable, high-impact solutions across ingestion, modeling, and delivery.

50%Workflow efficiency gain

40%Model size reduction

95%Accuracy in model selection

Experience

Shipping data and ML that scale

From research to production APIs, I align data pipelines, modeling, and developer experience to deliver measurable impact.

Jan 2025 – Present

Grand Valley State University

Documented and maintained data collection protocols for AI experiments, boosting reproducibility and efficiency by 30%.
Built automated data collection and analysis workflows with Python, Pandas, and Scikit-learn, halving processing time and reaching 95% accuracy in model selection.
Applied knowledge distillation, quantization, and fine-tuning to shrink model size by 40% and accelerate inference by 50%.

Aug 2025 – Dec 2025

Grand Valley State University

Designed and optimized REST/GraphQL routers to cut response latency and improve throughput under production-like load.
Built reusable API adapters and middleware for auth, validation, and error handling to speed up integration for new endpoints.
Aligned backend contracts and typing with frontend adapters to deliver reliable, typed client-facing features.

Aug 2024 – Dec 2024

Yiddish Arts and Academics Association of North America

Implemented scalable data collection and storage on Firebase for 500+ user-generated records across accounts, media, and messaging.
Produced clear technical documentation that streamlined onboarding and reduced knowledge transfer time.
Partnered with cross-functional stakeholders to convert requirements into analytical frameworks and data visualizations.

Projects

Experiments and production-minded prototypes that explore data systems, graph analytics, and efficient ML.

Transformed citation data with Python (Pandas, NumPy) and built a Neo4j graph for visualization and influence analysis.

Highlighted key academic influencers and trend insights.

Fine-tuned EfficientNet on PlantVillage and distilled to a lightweight deployable model.

Reduced inference latency by 40% while keeping accuracy deployment-ready.

Skills

A stack tuned for building, shipping, and observing data-intensive systems.

PythonSQLGitLinuxAPIsVS CodeJupyter

Apache AirflowETL/ELTSnowflakeData Modeling

Scikit-learnPyTorchTensorFlow/Keras

AWSNeo4jFirebase

Education

B.S. in Computer Science · Minor in Mathematics · GPA: 3.82 · Dean’s List · Graduation: December 2025

Let's build

I love hard problems at the intersection of data infrastructure, modeling, and product impact.