Thomas Eric Email & Phone Number

United States

Microservices Architecture and Management: Designed and managed a complex technology stack consisting of over 150 microservices. Played a pivotal role in implementing over 100 microservices using Java (Spring Boot, Spring Data, Spring Security) and Kotlin, ensuring modularity, scalability, and efficient communication across the platform. Leveraged Docker, Kafka, and RabbitMQ for seamless integration and performance.Medical Data Platform Development: Built a robust platform for submitting and receiving medical data in diverse formats. Facilitated real-time data exchange between hospitals, physicians’ offices, and laboratories using REST APIs and message brokers like JMS, ActiveMQ, and RabbitMQ, enabling better interoperability and communication across the healthcare ecosystem.Product Team Management: Managed four product teams, driving the successful development and rollout of multiple projects. Provided technical leadership and guidance, ensuring efficient project execution from initial build phases to final delivery, while fostering cross-functional collaboration.Cloud-Based Platform for Interface Management: Designed and developed a cloud-based platform for managing interfaces, utilizing AWS and Spring Cloud to enhance scalability and flexibility. Simplified the integration of diverse data interfaces, ensuring secure and efficient operations.Machine Learning API Development: Developed and deployed machine learning APIs to enhance data analysis and workflow automation. Leveraged tools like Spring Boot, ElasticSearch, and Kotlin for efficient integration of predictive models into existing platforms.Technology Stack Oversight: Oversaw the implementation and maintenance of a large-scale technology stack, including Gradle, Maven, GitHub Actions, Jenkins, and Tomcat. Ensured all components met high performance, reliability, and security standards. Optimized CI/CD workflows for seamless deployments and system stability.

United States

Scalable Machine Learning API Development: Adapted an API to deploy a scalable, cloud-based machine learning model using Go, Kubernetes, and Docker. Implemented a mechanism for the scheduled execution of the API with Apache Airflow, enhancing model deployment efficiency and automation.Redis Cache Integration: Worked on the back-end process for writing data to an in-memory Redis cache using Java. Improved data retrieval speed and application performance by leveraging Redis for fast, temporary data storage.Machine Learning Metrics Development: Developed exploratory data analysis (EDA) and validation metrics for a machine learning model, utilizing Java and H2O. Ensured the model’s effectiveness and accuracy for a daily email job, supporting data-driven insights and reporting.Spark Job Migration: Migrated Spark jobs from GCP-managed Dataproc to Google Kubernetes Engine (GKE). Transitioned from a managed Spark environment to a customized, team-managed setup, improving flexibility and control over Spark job execution.GKE Cluster Integration:Integrated Kubecost, Prometheus, and Istio into a GKE cluster to enhance the data science team’s Kubeflow capabilities. Provided comprehensive monitoring, cost management, and service mesh functionalities for the Kubernetes environment.NLP-Based SKU Matching: Developed natural language processing (NLP) based algorithms for SKU similarity matching. Facilitated best price promises by analyzing competitor pricing and optimizing product offerings.Cloud Infrastructure Monitoring: Participated in on-call rotations and DevOps monitoring for GCP and AWS cloud infrastructure. Ensured the stability and performance of cloud services through proactive monitoring and issue resolution.Scheduled API Execution: Implemented scheduled execution mechanisms for APIs using Apache Airflow. Automated regular tasks and ensured timely updates and processes, enhancing overall operational efficiency.

Florida, United States

Early-Stage Startup Support: Served as a stopgap in an early-stage startup while the founders sought dedicated employees. Provided critical support across various functions, ensuring continuity and progress during a transitional period.Cross-Functional Discussions: Engaged in discussions with the team covering a wide range of topics, including machine learning, software engineering, and DevOps strategies. Contributed to shaping the startup’s technical direction and approach.Machine Learning Pipeline Development: Developed a pipeline to integrate machine learning capabilities into the flagship application. Enabled advanced data processing and model deployment, enhancing the application's functionality and performance.Collaboration with Industry Veterans: Worked alongside an accomplished team of tech industry veterans. Gained valuable insights and knowledge from experienced professionals while contributing effectively to the team's objectives.AWS Ecosystem Utilization: Leveraged the AWS ecosystem to build scalable solutions with flexible architecture. Designed systems with loose coupling, allowing for easy transitions to locally-hosted solutions or alternative vendors if needed.Command Line Tool Development: Created command line tools for submitting and monitoring machine learning jobs. Simplified job management and tracking, improving the efficiency and usability of ML workflows.Open Source Service Mesh Development: Developed an open source service mesh, which was forked and customized internally. Contributed to creating a tailored solution for managing microservices and service communications.Learning and Value Addition: Maximized learning opportunities and provided significant value during a crucial period for the startup. Applied acquired knowledge to deliver effective solutions and support the company's growth.

United States

- Developed a TWAIN driver for electronic signature processing.- Created tools for processing messy text data.- Implemented rudimentary web applications for marketing and internal use.- Fixed bugs in the production application.- Helped implement new features in the production application.