Senior AI/ML Engineer Clinical Health Systems

Colby Lee

Bean

Staff AI / ML

Engineer

**********@*******.***

Winter Park, FL

SUMMARY

Staff AI/ML Engineer specializing in

clinical AI systems that improve

patient outcomes and reduce

operational burden across large

health networks. Experienced

building HIPAA-compliant,

production-grade solutions from

Clinical NLP and readmission risk

models to GenAI-powered EHR

assistants and medical imaging

pipelines. Skilled at bridging clinical,

technical, and executive stakeholders

to deliver AI systems that earn trust

and drive measurable results at scale.

EDUCATION

Bachelor of Science (B.S.) in Computer Science

Stetson University

01/2014 – 02/2018 DeLand, FL

PROFESSIONAL EXPERIENCE

Staff AI / ML Engineer

AdventHealth – Information Technology / Digital

Health

02/2025 – 02/2026 Altamonte Springs, FL

AdventHealth is one of the largest not-for-profit health systems in the U.S., operating 57 hospital campuses across 9 states.

•Architected a production Clinical NLP pipeline using fine- tuned LLMs and Named Entity Recognition (NER) to

extract structured diagnoses, medications, and procedures from unstructured physician notes - cutting manual chart review time by 60% and accelerating downstream coding and billing workflows

•Built and deployed an ensemble readmission risk model

(XGBoost + LightGBM) predicting 30-day hospital

readmission probability, enabling care coordination teams to intervene proactively with high-risk patients across all 57 hospital locations

•Led end-to-end design of a RAG-based GenAI clinical assistant integrated directly with the EHR, enabling clinical staff to surface patient history summaries and evidence-based care pathway recommendations at the point of care

•Developed a CNN and Vision Transformer (ViT) imaging pipeline supporting radiologists in anomaly detection across X-ray and MRI studies - improving detection throughput by 35% and reducing radiologist review burden

•Partnered with the CIO, clinical informatics, and compliance teams to establish AI Governance standards, model

monitoring frameworks, and deployment protocols fully compliant with HIPAA and clinical regulatory requirements Senior AI/ML Engineer

UKG (Ultimate Kronos Group)

10/2019 – 12/2024 Orlando, FL

UKG serves over 80,000 organizations globally, including a large base of hospital systems managing complex hourly workforces. SKILLS

•Languages & Query: Python, SQL,

Bash

•Healthcare AI: Clinical NLP, EHR

Integration, HIPAA Compliance, AI

Governance, Medical Imaging

Analysis, Readmission Prediction,

Clinical Decision Support, RAG for

Clinical Workflows

•Machine Learning & Modeling:

PyTorch, TensorFlow, Scikit-learn,

Keras, XGBoost, LightGBM,

Isolation Forest, Autoencoders,

Reinforcement Learning, Anomaly

Detection, Time-Series Forecasting,

Classification

•Generative AI & NLP: Hugging

Face, BERT, Sentence-BERT,

LLaMA, GPT, Fine-tuning,

LangChain, Prompt Engineering,

OpenAI API, Vector Databases,

NER, Semantic Search

•Computer Vision: CNN, Vision

Transformer (ViT), Object

Detection, Medical Imaging

Analysis

•MLOps & Infrastructure:

MLflow, Kubeflow, AWS

SageMaker, GCP Vertex AI,

Docker, Kubernetes, Apache

Airflow, CI/CD, GitHub Actions,

Model Drift Detection, Feature

Stores, A/B Testing

•Data Engineering: Apache Spark,

Kafka, dbt, ETL Pipelines,

PostgreSQL, Snowflake, Redis,

Pinecone, BigQuery

•Designed and shipped an LLM-powered RAG assistant enabling employees to query HR and benefits policies in natural language - reducing HR support ticket volume by 40%, including large hospital networks managing thousands of clinical and non-clinical staff

•Built a Reinforcement Learning and constraint satisfaction model for automated workforce scheduling - cutting scheduling time by 50% and improving labor compliance for large hourly workforces, including healthcare shift environments

•Developed an unsupervised anomaly detection system

(Isolation Forest + Autoencoders) identifying payroll fraud and processing errors in real time - directly applicable to healthcare billing integrity and claims accuracy

•Engineered a Sentence-BERT transformer-based candidate- to-job matching pipeline, improving recruiter placement accuracy - transferable to clinical staffing and credentialing workflows

•Trained a gradient boosting attrition model identifying high turnover risk 90 days in advance - enabling proactive HR intervention critical for health systems facing nursing and clinical staff retention challenges

•Mentored 4 ML engineers; led architecture reviews, code standards, and quarterly ML roadmap planning in

collaboration with product leadership

ML Engineer

Chewy

05/2018 – 08/2019 Orlando, FL

•Built a personalized recommendation engine using

collaborative filtering and deep neural networks (DNN), driving measurable lift in average order value and repeat purchase rates across millions of customers

•Developed a time-series demand forecasting system (LSTM + Prophet) predicting SKU-level inventory needs across 15 distribution centers - reducing overstock costs and improving fulfillment accuracy

•Designed a customer churn classification model enabling targeted retention campaigns that improved 90-day retention rates by double digits

•Collaborated with data engineering to build scalable ETL pipelines and feature stores supporting real-time ML inference in production

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