Engineering Work
Systems Portfolio
A focused collection of observability, signal intelligence, AI-assisted operations, and production systems work built through Minimalism.
Selected Experience
Built from real production systems work.
Senior Site Reliability Engineer
Wells Fargo
- Built ML-driven signal classification using Splunk MLTK.
- Reduced alert fatigue through signal prioritization and severity cadence.
- Led observability strategy across production platform services.
- Developed telemetry analysis and operational insight automation in Python.
Core Technologies
Productized Service
Signal Audit
A structured operational review that turns messy telemetry, incidents, and system behavior into clear engineering decisions.
View Signal Audit →Workflow Integration
Slack Integration
A Slack-based workflow for running Signal Audits directly inside engineering channels and incident review threads.
Free Tool
Signal Interpreter
A lightweight AI-assisted interpreter that classifies operational signals, explains what matters, and recommends next action.
Try Signal Interpreter →Published Thinking
UX Magazine Article
Published article exploring why AI systems fail silently and why observability must evolve beyond traditional monitoring.
Read Article →Architecture
Technical Architecture Diagrams
Visual breakdowns of signal flow, telemetry sources, Slack workflow behavior, audit processing, and output structure.
Artifacts
Example Outputs
Sample Signal Audit responses showing system overview, signal classification, observability gaps, risk assessment, and recommended actions.
What this portfolio demonstrates
Production Systems Thinking
Designed around real operational failure modes, service behavior, telemetry interpretation, and incident response.
AI-Assisted Operations
Uses AI to support engineering judgment instead of replacing it — turning noisy inputs into structured decisions.
Observability Strategy
Connects metrics, logs, traces, Kubernetes events, and human workflow into a clearer operational model.
Industry Recognition
Signal Audit is not a random AI project. It comes from real production experience interpreting telemetry, incidents, service behavior, and operational risk.
Interested in applying this to your environment?
Talk directly with the creator of Signal Audit.
Discuss your operational challenges and learn how Signal Audit can identify patterns hidden inside your production telemetry, incidents, and system behavior.
Schedule a Signal ReviewApply The Same Thinking To Your Environment.
The systems featured here represent years of experience in observability, incident response, telemetry analysis, reliability engineering, and operational systems.
Signal Audit applies those same techniques to help teams identify operational risk before it becomes an incident.