Eleven years shipping enterprise platforms for Verizon, JPMC, Capital One, AmEx and TGen — now pointing that discipline at GenAI systems.
I lead cross-functional teams that break monoliths into microservices, move workloads onto AWS, and put clean, fast interfaces in front of millions of users. From reactive Java 21 backends to micro-frontend React apps, I've spent a decade making enterprise software feel less enterprise — and more like a product people actually enjoy using.
Sr. Lead Full Stack Java Developer · Irving, TX
Leading North Star Architecture delivery across SOE, CXP and MVO — React micro-frontends over reactive Java 21 + Spring Boot services, high-volume Spring Batch ingestion, and mentoring across the squad.
Full Stack Engineer · Dallas, TX
Microservice architecture for the shared E2E data platform — Eureka + Zuul routing, voltage-encrypted REST with CORS/XSS/CSRF hardening, Dockerized services shipped through Jenkins to AWS.
Full Stack Java Developer · Remote
Drove the CaaS1 → CaaS2 content-platform migration on Nuxeo — target-architecture documentation, Java 8 schema validation, and guiding domain teams through the change.
Full Stack Java Developer · Phoenix, AZ
Built the physician portal for PHI-grade lab data with end-to-end RBAC — a monolith decomposed into Dockerized microservices with event-driven AWS Lambda, S3, CloudWatch and Route 53 failover.
Full Stack Java Developer
Raised SLAs on the high-traffic Offers API with an Apache Camel + Spring Boot re-architecture — Angular 6 Journey UI, APIGEE-secured endpoints, HBase + Hive persistence.
Java 8 → 21, Spring Boot & Cloud, reactive WebFlux, Spring Batch, JPA + Hibernate, Apache Camel. Monolith-to-microservice migrations done four times at four companies.
Micro-frontend React at Verizon; Angular 2 → 21 across five clients. TypeScript, HTML5/CSS3, accessible cross-browser interfaces.
Lambda, S3, EC2, SQS/SNS, DynamoDB, Aurora, CloudFormation, CloudWatch, Route 53 — with Docker, Kubernetes, Jenkins and APIGEE in the pipeline.
Oracle, Postgres, MySQL and DB2 on the relational side; MongoDB, DynamoDB and HBase + Hive for high-volume risk data.
GoF + JEE design patterns, TDD with JUnit/Mockito, Sonar quality gates, Agile/Scrum leadership and hands-on mentoring.
LLM APIs, prompt engineering and structured outputs — wired into the Java, Spring and React systems I already ship.
› Claude · GPT · Spring AIRetrieval pipelines, chunking, embeddings and context design over enterprise data stores.
› LangChain · pgvectorMulti-step agents with tool use, planning and orchestration — production patterns, not demos.
› LangGraph · MCPLoRA / QLoRA adaptation and local inference for private, cost-controlled deployments.
› Unsloth · OllamaRetrieval-quality scoring and full-trace observability — treating AI quality like uptime.
› RAGAS · Langfuse · PhoenixGuardrails, PII handling and red-teaming — the posture I applied to PHI and payments data.
› NeMo · Presidio · GarakThe thesis is simple: the teams that win with AI are the ones that already know how to run production systems. A decade of encrypted data transfer, RBAC, observability and CI/CD is exactly the foundation GenAI engineering needs.