Professional Profile
Lead Data and MLOps Engineer specializing in production-grade generative AI, machine learning, and data platforms. Designs secure evaluation, monitoring, inference, and analytics solutions across AWS, Snowflake, and Apache Airflow, with a strong record of partnering with data scientists and business stakeholders to operationalize reliable, governed AI systems.
Generative AI
Evaluation, agents, enterprise analytics
MLOps Platforms
Monitoring, inference, model operations
Cloud & Data
AWS, Snowflake, Airflow, Terraform
Professional Experience
Lead Data Engineer (MLOps) at Zelis
Aug 2021 – Present Remote
Lead MLOps and data engineering initiatives that operationalize generative AI and traditional machine learning solutions across AWS, Snowflake, and Apache Airflow.
Selected AI/ML Platforms
- GenAI Evaluation Platform: Developed an enterprise platform for evaluating company GenAI applications and custom GPTs, including a reusable Python metrics library, AWS Step Functions ETL workflows, and an AWS-hosted Streamlit dashboard with SSO and role-based access control.
- Model Monitoring Platform: Designed daily data drift and model performance monitoring with Airflow, AWS Lambda, Amazon ECS, a custom Snowflake data model, Streamlit reporting, and automated email alerts for critical model issues.
- Batch Inference Pipelines: Built Airflow-orchestrated SageMaker and SageMaker Pipelines workflows for traditional ML inference, persisting prediction outputs to Snowflake for reporting and downstream analysis.
- Agentic Jira Assistant: Created a Streamlit application powered by a custom AI agent that uses project context and brief user requests to generate structured Jira descriptions, acceptance criteria, and implementation details.
- ChatGPT Logging & Secure Explorer: Implemented daily Amazon ECS jobs using the OpenAI Compliance API to retrieve Enterprise conversations, store standardized JSON in Amazon S3, generate custom GPT datasets, and provide authorized users an authenticated search, view, and download interface.
Engineering Enablement & Internal Platforms
- Private PyPI Repository: Established an AWS-hosted package index for distributing internal Python source and wheel packages through pip within the corporate network.
- ChatGPT Usage Dashboard: Built an AWS-hosted Streamlit application that reports OpenAI Enterprise adoption, usage patterns, and business-purpose metrics.
- Team Deployment Portal: Developed a Flask application integrated with GitHub Enterprise and GitHub Actions to trigger environment-specific deployments, persist user and workflow metadata, and display asynchronous deployment status.
- Team Documentation Portal: Created a Flask knowledge hub for applications, processes, databases, and SOPs, with executive-friendly technical content, interactive Mermaid diagrams, and cross-category tag filtering.
Platform Engineering, Governance & Delivery
- Security & Access Governance: Implemented SSO, role-based access control, and restrictive AWS permissions to protect sensitive enterprise AI data and internal applications.
- Cloud Infrastructure & Delivery: Automated repeatable application and platform deployments with Terraform, Docker, Amazon ECR, ECS, Lambda, IAM, SSO, and CI/CD workflows.
- Large-Scale Data Processing: Developed Spark and Amazon EMR pipelines to process high-volume datasets and support scalable analytics and machine learning workflows.
- Snowflake & Airflow Engineering: Designed database structures and production data pipelines that integrate Snowflake with Apache Airflow for dependable, repeatable processing.
- Cross-Functional Delivery: Partnered with data scientists, engineers, and business stakeholders to productionize models and deliver secure, maintainable AI/ML systems.
Senior Data Analyst at C.H. Robinson
Jan 2014 – Aug 2021 Remote
- Conducted exploratory analysis in Jupyter to identify trends and deliver actionable business insights.
- Built interactive analytics applications and dashboards with Plotly, Streamlit, Power BI, and R Shiny.
- Optimized SQL queries and data workflows across SQL Server, PostgreSQL, and Hive environments.
- Developed Python-based ETL, data integration, and automated reporting solutions.
- Improved data quality by integrating and validating disparate data sources and translating stakeholder requirements into scalable solutions.
Technical Skills
Programming & Data Engineering
Python
SQL
Apache Airflow
Spark
Docker
Bash
ETL/ELT
API Integration
Generative AI & MLOps
GenAI Evaluation
AI Agents
Model Monitoring
Data Drift
Batch Inference
Usage Analytics
AWS
SageMaker
Step Functions
ECS
ECR
Lambda
S3
IAM
RDS
Glue
EMR
Data Platforms
Snowflake
PostgreSQL
SQL Server
Hive
Database Design
Data Modeling
Applications & Visualization
Streamlit
Flask
Jupyter
Plotly
Power BI
R Shiny
Mermaid
Infrastructure & CI/CD
Terraform
GitHub Actions
GitHub Enterprise
SSO
Role-Based Access Control
AI-Augmented Engineering Practices
- AI Pair Programming: Use Cursor, GitHub Copilot, and Codex to accelerate test generation, boilerplate, refactoring support, and routine engineering tasks while maintaining review ownership of production code.
- Agent-Assisted Documentation: Direct coding agents to draft technical documentation, summarize implementation details, and explain completed work to improve maintainability, handoffs, and team knowledge sharing.
Education
University of Massachusetts Lowell
B.S. Information Technology
- Focus: programming and relational databases
University of Oklahoma
Programming and relational database coursework
- Research assistant, NSSL
- Minor in Mathematics