Alex Woelkers
CS (B.S.) senior @ UCSC - graduating June 2026
Early-career software engineer grounded in systems and networking, building backend infrastructure
30-second summary
- • Software engineer intern at Esri, shipped production-ready Java REST APIs implementing OGC standards for global enterprise GIS customers.
- • Undergraduate researcher in UCSC's Inter-Networking Research Group, working on distributed simulation and networking systems (RabbitMQ), including edge intelligence and federated learning.
- • Owned backend services end-to-end, including CI/CD, testing, and reliability improvements across industry, research, and personal projects.
- • Lead officer in UCSC's Computer Networking Student Association; organized guest speakers from academia and industry and created a by-students, for-students mentorship program, scaling it up to 20 mentors in collaboration with ACM and Google Developer Student Club.
- • Comfortable taking ambiguous technical problems from specifications or research to working, measurable, validated systems.
Core strengths
Distributed systems and networking in practice
Hands-on experience working with distributed and networked systems through research, simulations, and production-facing projects (e.g., message queues, edge-oriented systems, federated learning).
Independent execution with scoped ownership
Proven ability to take ownership of technically ambiguous projects, define scope, implement incrementally, and validate correctness - working effectively in hands-off environments with periodic alignment rather than constant supervision.
Fast technical ramp from goals to validated systems
Strong at decomposing high-level technical goals, learning unfamiliar systems through documentation and conversations, and iterating toward measurable, working implementations while continuously validating direction and assumptions.
How I work
- - I start with an unclear technical goal, decompose it into testable pieces, and validate direction early.
- - I learn new systems quickly through documentation, experiments, and conversations with domain experts.
- - I prefer shipping incremental, observable progress over large unvalidated changes.
- - I design for failure and debuggability, not just happy-path functionality.
- - I actively seek feedback when scope or assumptions need correction.
Featured work
Esri - OGC API Features
Software Engineer Intern
Enable standards-based, cross-platform access to hosted feature services by implementing the OGC API Features specification within Esri's enterprise GIS platform.
Esri - OGC API Features
Software Engineer Intern
Enable standards-based, cross-platform access to hosted feature services by implementing the OGC API Features specification within Esri's enterprise GIS platform.
Problem
Enable standards-based, cross-platform access to hosted feature services by implementing the OGC API Features specification within Esri's enterprise GIS platform.
What I owned and built
- Implemented production-ready Java REST APIs supporting core and advanced OGC API Features endpoints.
- Designed translation layers mapping OGC query parameters to Esri's internal feature service APIs.
- Built full CQL2 filtering support using a custom ANTLR4 grammar.
- Implemented paging, content negotiation, and link generation.
Execution model
- High autonomy and full ownership of the feature area.
- Interpreted formal specs, coordinated across teams, consulted OGC experts.
- Incremental validation against real clients.
Validation and reliability
- End-to-end testing with QGIS and Leaflet.
- Mitigated CRS, filtering, pagination, and format edge cases.
- Maintained production-level reliability.
Outcome
- Delivered a standards-compliant API enabling cross-platform GIS data access.
- Established a foundation for further compliance testing and adoption.
Network Simulation Bridge - RabbitMQ Architecture Redesign
Undergraduate Researcher, UCSC Inter-Networking Research Group
Legacy socket-based messaging limited reliability and scalability for distributed simulations.
Network Simulation Bridge - RabbitMQ Architecture Redesign
Undergraduate Researcher, UCSC Inter-Networking Research Group
Legacy socket-based messaging limited reliability and scalability for distributed simulations.
Problem
Legacy socket-based messaging limited reliability and scalability for distributed simulations.
What I owned and built
- Led redesign to a RabbitMQ-based messaging backend.
- Designed broker-driven routing architecture.
- Preserved API semantics while replacing transport layer.
- Built topic-based queues and optional Redis-backed payload storage.
Architecture and tradeoffs
- Traded overhead for improved fault tolerance and scalability.
- Removed daemon from message delivery path to reduce coupling.
Execution model
- Primary driver; independently learned RabbitMQ internals and proposed redesign.
- Refactored legacy code without breaking existing semantics.
Validation and results
- Extensive local testing under failure scenarios and large payloads.
- Improved robustness vs socket-based version.
- Adopted as preferred backend for NSB.
Outcome
Delivered a scalable, modern messaging architecture for research and simulation.
GridAI - Cloud-Native Grid Monitoring and AI Summarization
Personal Project
Public grid data is hard to interpret for operators and residents in real time.
GridAI - Cloud-Native Grid Monitoring and AI Summarization
Personal Project
Public grid data is hard to interpret for operators and residents in real time.
Problem
Public grid data is hard to interpret for operators and residents in real time.
What I built
- Cloud-native pipeline ingesting hourly EIA grid data across 45+ regions.
- Kubernetes-based ingestion with normalization and persistence.
- FastAPI backend and Next.js frontend dashboards.
- LLM-based structured operational and resident summaries.
System architecture
- Hourly CronJob → EIA API → S3 (raw) → DynamoDB (normalized) → LLM summaries.
- EKS deployments for ingest, API, frontend.
- Prometheus metrics and ingest heartbeats.
AI/LLM usage
- Structured JSON outputs for history, forecast, resident translation.
- Defensive parsing and non-fatal failure handling.
Execution model and tradeoffs
- End-to-end ownership of infra, data modeling, prompts, and deployment.
- Managed IAM, API limits, time zones, architecture mismatches.
- Traded simplicity for cost and freshness for reliability.
Outcome
- Deployed production system on AWS.
- Hands-on experience operating Kubernetes and AI-driven pipelines.
- Clear roadmap for future improvements.
What I'm looking for next
I'm seeking an entry-level software engineering role at an early-stage or growth-stage company, and I'm also open to established teams, where junior engineers are trusted with meaningful ownership and expected to engage deeply with real systems in production.
I'm most interested in backend- and systems-oriented roles, including work on distributed systems, infrastructure, and reliability-sensitive services. I have experience building frontend applications, but I'm not optimizing for frontend-only roles.
I want to deepen my backend and distributed systems knowledge and learn how to design, operate, and scale systems in real-world environments. I do best in settings where technical goals may start ambiguous but are refined through iteration, feedback, and validation.
I'm open to roles across locations, with a preference for the Bay Area, San Diego, Boston, Los Angeles, Seattle, or New York City. My earliest start date is August 2026.
I also have interests in IoT, autonomous robotics ( Somars ), applied AI/ML, edge AI, GIS, entrepreneurship, and many other topics. Tons of my side projects focus on these topics, and I'd enjoy talking about them with you!