I turn research and machine-learning prototypes into reproducible engineering workflows, with a focus on evaluation, reliability, and real technical data.
Industrial AI / Agent Systems / Case-Based Reasoning
Problem: Standard document retrieval surfaces passages without preserving the reasoning process required for technical tasks.
Contribution: Co-engineered procedure retrieval, objective evaluation, and fallback logic around the LLM, reducing unnecessary LLM usage through selective context and caching.
Industrial AI / Time-Series Machine Learning / Model Validation
Problem: CAE simulation assessment requires evaluating complex multichannel time-series signals alongside tabular test parameters.
Contribution: Learned expert assessment patterns from multichannel simulation signals and tabular context using modular time-series models and bootstrapped confidence intervals.
Industrial AI / Graph Neural Networks / Scientific ML
Problem: High-dimensional crash simulation requires rapid approximate predictions for mesh deformation and time-dependent acceleration.
Contribution: Developed and evaluated GNN surrogate workflows for selected crash-simulation outputs using diagnostic evaluation tools.
Industrial AI / MLOps
Problem: Crash-test sensor channel validation requires a deployable inference path combining signal imagery and tabular features.
Contribution: Built containerized AWS Lambda signal processing and classification workflows for crash-test sensor time-series data with Pydantic event validation.
LLM workflows, retrieval, structured evaluation, and fallback systems.
Time-series classification, signal processing, and simulation data.
Reproducible experiments, model diagnostics, and deployment workflows.
I am an AI Engineer working across LLM workflows, industrial time-series data, simulation, and model evaluation.
My focus is not only model development, but the engineering systems that make AI workflows reproducible, inspectable, and useful.