Available to hire
I am a Senior AI/ML Engineer with over a decade of experience building scalable ML workflows, NLP systems, and data platforms in healthcare and enterprise settings. I design and deploy AI-enabled features across AWS, Azure, and GCP, focusing on robust data pipelines, reproducible research, and compliant AI outputs.
I thrive in cross-functional teams, accelerating ML experimentation, optimizing data processing, and delivering impact through reliable, ethically sound AI solutions. My work spans from clinical data platforms and NLP chatbots to predictive analytics and real-time data ingestion for telehealth platforms.
Skills
Language
English
Fluent
French
Advanced
Spanish; Castilian
Beginner
Work Experience
Senior AI/ML Engineer at TelevisitMD
January 1, 2025 - December 31, 2025Created an enterprise healthcare data platform on Azure, consolidating data from AWS and GCP into a unified analytics environment supporting 20k+ records. Developed and maintained NLP and ML systems using Python, PyTorch, and Hugging Face (T5, BERT, SBERT), analyzing over 5 million clinical and operational text records. Built data pipelines using Python, SQL, and Hadoop, reducing processing time by 35% and accelerating ML experimentation cycles. Automated end-to-end deployment of four critical ML pipelines using Terraform and IAM, increasing deployment frequency by 30% and reducing infrastructure costs by 15% across environments. Delivered AI-enabled features for telehealth platforms, supporting video consultations, secure messaging, and real-time patient data ingestion within Agile teams.
Senior ML Engineer at Sparrow Hospital/University of Michigan Health
January 1, 2020 - December 31, 2024Drove the application of innovative ML techniques to structured datasets, accelerating data processing time by 45% across 15+ multinational clinical research projects. Led the development of NLP and ML solutions using Python, TensorFlow, Keras, and scikit-learn, including a chatbot that reduced response time and improved operational efficiency. Contributed to the training and evaluation of GANs and anomaly detection models, generating synthetic test data and reducing testing costs by over 20%. Collaborated with researchers and engineers on data collection, reporting, and regulatory compliance to ensure reproducible, ethical ML outputs. Executed feature engineering and model validation for predictive analytics, contributing to a 23% reduction in unplanned downtime.
Data Scientist at Northwest Houston Heart Center
January 1, 2019 - December 31, 2020Conducted exploratory and statistical analysis on high-dimensional healthcare datasets to identify patterns and risk signals supporting over 10 clinical and operational initiatives. Developed predictive and descriptive models to improve forecast accuracy and decision-making. Applied advanced feature construction on temporal and categorical data, enhancing signal quality and downstream model performance. Designed and evaluated experimental frameworks to ensure statistically sound, reproducible insights. Partnered with domain experts to translate analytical findings into actionable recommendations.
Data Engineer at Michigan State University
January 1, 2016 - December 31, 2019Architected scalable ETL workflows ingesting structured and semi-structured data from clinical systems, APIs, and cloud storage, processing over 3 million records annually. Designed cloud-native data models and analytics tables, improving query performance for downstream reporting and modeling use cases. Implemented automated data validation, schema evolution handling, and pipeline observability, reducing data quality incidents by 55%. Integrated batch and near-real-time ingestion pipelines using Airflow, delivering analytics-ready datasets faster. Optimized storage and compute usage through partitioning and lifecycle policies, lowering annual data infrastructure spend by ~20%.
AI Trainer at Michigan State University
January 1, 2015 - December 31, 2016Supported AI training initiatives within multinational clinical research programs by curating, annotating, and validating datasets used for ML and decision-support models. Collaborated with researchers and data scientists to define labeling guidelines, quality checks, and evaluation criteria, ensuring consistency across multi-center clinical datasets. Prepared ML-ready data from clinical trial records by cleaning, structuring, and reviewing large volumes of data. Monitored model outputs for accuracy, bias, and compliance, providing feedback to improve training workflows. Conducted 50+ audits of AI algorithms to identify and mitigate potential biases, improving fairness and transparency in clinical decision-making processes.
Education
Bachelor of Science (BSc Honors) at McGill University
January 1, 2010 - January 1, 2014Qualifications
Programming for Everybody – Python
January 1, 2015 - December 31, 2015R Programming for Data Science – IBM
January 1, 2015 - December 31, 2015Industry Experience
Healthcare, Life Sciences, Education, Professional Services, Software & Internet
Skills
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