Cleared U.S. cyber operations specialist building secure AI infrastructure, mission decision systems, and performance-sensitive engineering projects.

I am strongest where backend/platform engineering meets high-stakes operations: securing model-serving paths, measuring inference reliability, translating ambiguous workflows into operational software, and building deterministic systems that can be tested under pressure.

Resume website: wafflebits.github.io/WaffleBits

• AI infrastructure / platform reliability: model-serving gateways, inference benchmarks, Docker/Kubernetes-oriented deployment thinking, Prometheus-compatible artifacts, latency/throughput regression checks, and GPU-aware system design.
• Infrastructure security: authentication, authorization, RBAC, rate limits, audit trails, policy enforcement, threat modeling, secure service boundaries, and production extension paths such as OIDC, mTLS, external policy engines, and key management.
• Forward-deployed / mission engineering: turning ambiguous operational data into working tools for root-cause analysis, what-if planning, observable delivery, and decision support.
• Quantitative systems engineering: deterministic matching, market microstructure, latency measurement, correctness testing, Linux fundamentals, and a C++/Rust systems roadmap.

• Languages: Python, TypeScript, Java; actively building toward Rust/C++ for systems and quant work.
• Backend/platform: FastAPI, REST APIs, Docker, Linux, CI, service boundaries, testable architecture, and Kubernetes deployment shapes.
• AI infrastructure: Triton-style serving concepts, benchmarking, latency percentiles, throughput, failure accounting, Prometheus output, regression comparison, and GPU-aware reliability.
• Security: access control, policy enforcement, audit logging, rate limiting, public-safe threat modeling, incident response, and secure service design.
• Product judgment: synthetic operational data modeling, command-facing workflows, explainable recommendations, reviewer-friendly docs, and public-safe portfolio discipline.

• AI infrastructure and reliability teams: distributed services, model-serving reliability, Kubernetes-oriented operations, observability, inference benchmarking, and performance regression tracking.
• Infrastructure security teams: secure access paths, service boundaries, policy enforcement, audit evidence, threat models, incident runbooks, and controls around AI workloads.
• Forward-deployed AI / government engineering teams: cleared mission context, stakeholder translation, full-stack prototypes, observable systems, and delivery under ambiguous requirements.
• Quantitative systems teams: deterministic execution, market mechanics, performance measurement, strong CS fundamentals, and a path toward lower-level implementations.

Synthetic mission readiness platform that fuses sortie, maintenance, supply, personnel, and outage data into a command-facing decision surface.

Covers operational data modeling, FastAPI service design, React/TypeScript workflow design, root-cause scoring, what-if analysis, Docker, tests, and public-safe mission framing.

Distributed inference benchmarking toolkit for Triton-compatible model-serving workflows.

Covers Python load generation, configurable concurrency, retry-aware execution, p50/p95/p99 latency, throughput, success-rate reporting, JSON outputs, and a clean path from mock CI to live inference testing.

Includes Prometheus text export, baseline-versus-candidate regression reporting, operations notes, and a Kubernetes Job shape for cluster-local benchmark runs.

Security-focused AI infrastructure project for authenticated model access, RBAC, rate limiting, audit logs, policy checks, and observability.

Covers authenticated model access, per-model authorization, reason-for-access enforcement, rate limiting, structured audit logs, Prometheus-compatible metrics, Kubernetes health/scrape posture, SLO notes, incident runbooks, policy checks, tests, and production extension points such as OIDC, mTLS, KMS, GPU telemetry, and external policy engines.

Low-level matching engine and backtesting project for limit-order-book mechanics, deterministic execution, latency measurement, and market simulation.

Covers price-time priority, integer tick prices, partial fills, market orders, cancellations, deterministic snapshots, edge-case tests, and benchmarkable execution. Next phase is a C++20 or Rust core with latency histograms and replay-style market data ingestion.

1. Add warmup/cold-start windows, payload profiles, and server-side telemetry correlation to the Triton benchmark.
2. Add OIDC/JWT verification, distributed rate limiting, OpenTelemetry traces, and Grafana dashboard screenshots to the secure GPU inference gateway.
3. Extend the Kubernetes, metrics, SLO, and runbook pattern into the readiness repo.
4. Port the market microstructure core to C++20 or Rust and compare latency, memory layout, and throughput against the Python reference.
5. Prepare the local LLM inference-serving stack for public release only after README cleanup, tests, and a reviewer-safe benchmark report.

All public repositories use synthetic data, mock integrations, or open tooling. I do not publish operational, classified, proprietary, government-furnished, or sensitive customer data.