Tilson Technology Management Inc                                                                                                     Chandler, AZ

Senior Software Engineer                                                                                                  Aug 2023 - May 2025

• Architected and deployed machine learning solutions on AWS (S3, Glue, Lambda, SageMaker) and GCP (BigQuery, Vertex AI), leveraging Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs) to power a genetic risk prediction platform, processing millions of requests annually with 99% uptime and improving predictive accuracy.
• Developed and optimized deep learning models for personalized disease risk scoring, NLP report generation, and ancestry clustering using TensorFlow, PyTorch, Hugging Face, Scikit-learn, and XGBoost, increasing accuracy by 18% and reducing model inference time by 43%.
• Integrated LLMs and generative AI (OpenAI, Vertex AI, Hugging Face) into AI-powered chatbots, self-service tools, and customer support systems, leveraging NLP to reduce support ticket volume by 27% and improve user interactions.
• Built full-stack applications using JavaScript, React, Node.js, and FastAPI, ensuring seamless AI backend and frontend integration through RESTful APIs and GraphQL, supporting real-time health analytics for 14M+ users.
• Managed hybrid-cloud deployments with Docker, Kubernetes, and Terraform across AWS and GCP, ensuring HIPAA, GDPR compliance, and streamlining deployment pipelines with CI/CD for continuous delivery.
• Engineered big data ETL pipelines with Python, Pandas, AWS Glue, MongoDB, PostgreSQL, and BigQuery, reducing data latency by 55% and enabling high-performance genomics analytics on datasets exceeding 8 petabytes.
• Automated MLOps workflows using CI/CD tools like GitHub Actions, CircleCI, Docker, Kubernetes, and Terraform, optimizing model deployment and improving model monitoring with Prometheus, Grafana, and AWS CloudWatch for real-time performance tracking.
• Optimized cloud resource usage across AWS and GCP, reducing compute costs by 17%, while improving model performance, data quality, and data lineage through automated model retraining and model registry solutions.
• Collaborated on AI-driven prototypes for B2B solutions, integrating AI models with JavaScript and Python for both frontend and backend, leading to a $2.3M increase in API-driven revenue.
• Developed automated test suites using Pytest, Selenium, and Jest, ensuring 98%+ test coverage and high system reliability, including model monitoring to track model drift and performance in production with MLflow.
• Authored technical documentation on data flows, APIspecs, and MLOps playbooks, enhancing cross-functional collaboration and providing guidelines for model monitoring and efficient team onboarding.
• Mentored junior engineers, fostering best practices for model monitoring, code reviews, Agile processes, and ensuring high-quality standards in AI development and cross-functional product delivery.

Civis Analytics                                                                                                                                                    Chicago, IL   

Senior Machine Learning Engineer                                                                                      Feb 2022 – Feb 2023

• Fine-tuned Large Language Models (LLMs) like GPT-3 and BERT for a range of NLP applications, including sentiment analysis, named entity recognition (NER), and text classification, using TensorFlow and PyTorch to improve task accuracy and processing efficiency.
• Developed and deployed real-time sentiment analysis pipelines for customer feedback and social media data, leveraging Apache Kafka for data streaming, and GCP and AWS for scalable processing, improving decision-making based on real-time insights.
• Implemented advanced hyperparameter tuning for LLMs using GridSearchCV, Bayesian optimization, and learning rate schedules to fine-tune model performance, reduce overfitting, and ensure robust results across diverse datasets.
• Led the distributed training of LLMs on cloud platforms (AWS, GCP), utilizing GPU acceleration and model parallelism with TensorFlow and PyTorch to enhance training efficiency, reduce compute time, and enable large-scale model deployment.
• Developed a comprehensive model evaluation framework, utilizing metrics like precision, recall, and F1 score, while integrating tools like MLflow and Kubeflow for versioning, continuous monitoring, and tracking performance improvements throughout the model lifecycle.
• Fine-tuned LLMs for text generation tasks, enhancing user engagement through improved chatbotinteractions and personalized content generation, using transformers and attention mechanisms to deliver high-quality, contextually relevant outputs.                                                                                                              

Senior Data Engineer                                                                                                                    Oct 2020 – Feb 2022

• Contributed to the development of the C19VI (COVID-19 Vulnerability Index) using machine learning and big data technologies on Azure and GCP, utilizing Apache Spark, Azure Data Factory, and GCP BigQuery to process and analyze census and demographic data for improved predictive analytics and resource allocation.
• Engineered scalable data infrastructure solutions on Azure and GCP, implementing ETL pipelines with Apache Airflow, Talend, and Informatica, enabling seamless data integration and real-time processing across federated nonprofit organizations.
• Designed and implemented efficient ETL processes using Azure Data Factory, GCP BigQuery, and Apache Spark, optimizing the extraction, transformation, and loading of large datasets into data lakes and data warehouses for faster and more efficient data analysis.
• Built and optimized data pipelines for real-time streaming and batch processing on Azure and GCP, leveraging Apache Kafka, Docker, and Kubernetes to ensure high data throughput and low-latency integration, facilitating self-service analytics.
• Implemented data validation, data quality checks, and lineage tracking using Informatica, Collibra, and SQL to enforce data governance and ensure regulatory compliance for internal audits and reporting.

• Led the development of automated reporting solutions and data pipelines for compliance with MiFID II, SEC, and Dodd-Frank regulations, reducing manual intervention by 85%, and providing real-time data insights through Power BI and Tableau.

   

Intel Corporation                                                                                                                                  Chandler, AZ 

Data Engineer                                                                                                                       Nov 2017 - Mar 2020 

• Engineered and maintained cloud-based data lakes (AWSS3, Glue, EMR, Spark, Python, SQL) to process and store big data at scale, reducing analytics processing time by 40% and ensuring high availability in data storage solutions.
• Designed and automated ETL pipelines (Informatica, Python, Shell scripting, Oracle, SQL Server) for the extraction, transformation, and loading of data across multiple systems, improving data pipeline efficiency and ensuring smooth data integration for global teams.
• Optimized data workflows and real-time data processing solutions (Kafka, Spark Streaming, AWS Lambda) to power real-time dashboards and data reconciliation platforms for trade settlements and risk monitoring.
• Contributed to Intel nGraph by optimizing deep learning performance across multiple computing platforms (CPUs, GPUs, AI accelerators), enhancing the scalability of data pipelines for large-scale AI/ML projects.
• Implemented data quality checks and lineage tracking (Informatica, Collibra, SQL) for genomic data, ensuring data validation, improving regulatory compliance, and automating processes to increase audit readiness.
• Collaborated with cross-functional teams (businessanalysts, QA, DevOps, product managers) to develop data-driven solutions, automate regulatory reporting (MiFID II, SEC, Dodd-Frank), and enhance data governance frameworks for global compliance.