Experience
My professional journey in development and research
Full Stack AI/ML Engineer
Invene
McKinney, TX
Leading AI/ML development for healthcare monitoring solutions and medical imaging applications.
Key Achievements:
- Led development of real-time EEG stress/fatigue detection model deployed in ICU monitoring devices, achieving >90% precision and <200ms inference latency
- Built deep learning segmentation pipeline for brain MRI scans using U-Net, reducing manual annotation time by radiologists
- Engineered edge deployment system for cognitive state models on embedded Linux ICU hardware using Docker and PyTorch optimizations
- Developed clinician-facing dashboards using Plotly Dash to visualize EEG waveform states and segmentation masks for assisted diagnosis
AI/ML Engineer
Axxess Technology Solutions
Dallas, TX
Developed machine learning solutions for healthcare and financial applications.
Key Achievements:
- Built an NLP-based email triage system using fine-tuned BERT models to auto-classify support inquiries, saving 30+ hours/week in manual review
- Developed a model explainability layer using SHAP and LIME for loan default predictions, aiding compliance and analyst transparency
- Deployed scalable ML pipelines on GCP with FastAPI and Docker, supporting continuous retraining to adapt to data drift
- Collaborated with operations and support teams to integrate AI tools into production workflows with minimal latency overhead
Software Engineer
Iodine Software
Austin, TX
Focused on machine learning solutions for logistics and route optimization.
Key Achievements:
- Built an ETA prediction engine using historical delivery and GPS data, reducing ETA error from 30min to <10min through hybrid ML + ruleset approach
- Developed LSTM-based truck demand forecast model for regional logistics, improving fleet utilization by 18%
- Designed a geospatial clustering system for route optimization, cutting dispatch planning time and fuel consumption using HDBSCAN and OpenCV
- Integrated ML systems into Node.js-based dashboards and automated inference pipelines using AWS Lambda
Data Science Intern
Regional Healthcare Analytics Firm
Beaumont, TX
Applied machine learning to healthcare data for patient outcome prediction.
Key Achievements:
- Built a logistic regression model to predict 30-day readmission risk using EHR data, with ROC-AUC evaluation for deployment at partner hospitals
- Designed ICU mortality prediction models and collaborated with clinicians to validate medically relevant predictors
- Created interactive Jupyter dashboards for hospital leadership to monitor patient risk trends and care gaps
- Used Python, Scikit-learn, Pandas, and Jupyter Notebooks for medical code normalization and analysis