Principal Engineer & Independent Researcher
Principal Engineer in an S&P 500 enterprise focused on GenAI and agentic solutions using AWS services, Python, LangGraph, MCP, LLMs, and in-house AI infrastructure.
Profile Highlights
Organized around public work, current production AI platform responsibility, and the systems that connect research with shipping software.
S&P 500 enterprise
Building production GenAI and agentic systems using AWS services, Python, LangGraph, MCP, LLMs, and in-house AI infrastructure, while shaping secure, scalable platform capabilities inside a large public enterprise.
Public manuscripts
Research manuscripts and datasets across generative AI policy, RAG evaluation, hybrid cloud-edge intelligence, model editing, and knowledge agents.
Public engineering output
Published libraries for MCP security, RAG evaluation, LLM fleet routing, performance linting, tracing, schemas, and prompt contracts.
Verified reviewer activity
Reviewed 30+ AI-focused manuscripts across IEEE, ACM, and Springer venues, including journals and conferences with selective technical review standards.
Research & Publications
Publications and datasets across generative-AI policy, retrieval-augmented generation, hybrid cloud-edge intelligence, model editing, and knowledge-agent architectures.
8
Research works
7
Current preprints
8 publications and datasets
Academic Service & Peer Review
Serving as an invited reviewer for top-tier IEEE, ACM, and Springer journals and conferences — contributing rigorous, evidence-driven evaluations that strengthen the research community.
36
Total reviews
7
Venues served
3
Publishers
Invited Reviewer
Invited Reviewer
Invited Reviewer
Invited Reviewer
Invited Reviewer
Invited Reviewer
Invited Reviewer
Web of Science Academy
Elsevier BV (Netherlands)
Association for Computing Machinery
Serving as invited reviewer across IEEE, Springer, ACM
Engineering Open Source
12 published packages
External Open Source Contributions
apache/beam · #38748
Fixed a Python worker counter path so empty batches do not raise ZeroDivisionError during byte-size accounting.
Apache Beam's Python worker operation counters divided estimated batch byte size by batch length. When ReadFromCSV or another batched path produced an empty batch, batch_length could be zero and the worker counter update failed before the pipeline could continue.
Added an early return when the batch converter reports zero elements. The element counter still records the empty batch length, but the mean byte-size counter is left unchanged because there are no elements to average.
spring-projects/spring-integration · #11001
Corrected MQTT v5 inbound payload conversion so configured payload types are honored by the Spring Messaging converter contract.
The inbound MQTT v5 adapter used SmartMessageConverter.toMessage(...) when payloadType was configured. That is the outbound conversion path, so String payload targets could remain byte[] and JSON content could surface as confusing Base64 text.
Built an inbound Spring Message from the MQTT payload, converted it with SmartMessageConverter.fromMessage(...), preserved byte[] and MqttMessage pass-through behavior, and documented the breaking-change semantics for custom converters.
spring-projects/spring-batch · #5394
Prevented FlowStep execution from marking the enclosing job complete before later SimpleJob steps have run.
A job with a FlowStep followed by another step could expose the enclosing JobExecution as COMPLETED while additional steps were still pending, making downstream wait or monitoring logic unreliable.
Stopped FlowStep from directly completing the enclosing JobExecution. The contribution maps the flow result back to the step status and exit status while leaving the outer job lifecycle under SimpleJob control.
Experience Timeline
A concise progression through graduate systems work, platform engineering, principal engineering responsibility, and independent research output.
2014 - 2016
San Jose State University
Graduate foundation in software architecture, scalable systems, and disciplined engineering delivery.
2015 - 2017
Hewlett Packard Enterprise
Built data-intensive platform capabilities and production pipelines across enterprise engineering environments.
2017 - Present
S&P 500 enterprise
Builds GenAI and agentic solutions using AWS services, Python, LangGraph, MCP, LLMs, and in-house AI infrastructure. Designs scalable, secure infrastructure stacks for production AI platforms.
Current
Applied AI and open-source systems
Bridges production AI architecture with LLM evaluation, RAG reliability, cloud-edge AI behavior, and public developer tooling.
Technical Depth
Hands-on architecture depth across Java, Python, Spring Boot, Docker, Terraform, CI/CD, applied AI infrastructure, and data-intensive microservices.
AWS-first distributed systems with pragmatic reliability boundaries.
Data-intensive services built for maintainability and operational clarity.
Production-ready GenAI, agentic workflow, RAG, and evaluation pipelines with clear measurement and traceability.
Tooling that moves quality gates earlier into local development and CI.
From the Blog
Long-form writing on AI infrastructure, distributed systems, and the engineering trade-offs behind public libraries.
1 technical posts
Contact
Whether it's a research collaboration, open source contribution, or technical advisory — I'm always open to conversations that push the boundaries of AI and distributed systems.