Projected AWS runtime-cost reduction
Prevented a projected expansion to $962,641 annually.
Résumé
Production engineering is the foundation for my AI work.
My career foundation is data engineering, cloud infrastructure, geospatial systems, business intelligence, and production operations. At Verizon, I worked across AWS, Airflow, PostgreSQL/PostGIS, Python, distributed processing, containers, infrastructure as code, and on-call support.
The AI work on this site reuses those operating controls. Job queues, schema validation, release gates, recovery paths, and on-call habits became the structure around model calls.
Professional foundation
My engineering foundation spans cloud, data, geospatial, and platform systems.
At Verizon, I moved from GIS and network engineering into business intelligence management and the role of Principal Engineer, Data Science. I architected and operated AWS environments for national routing and planning workloads, with direct ownership across EC2, S3, SQS, RDS PostgreSQL, IAM, CloudFormation, CloudWatch, and VPC components.
Operational ownership
System ownership includes release and recovery.
At Verizon, I architected the initial production Airflow orchestration as Business Intelligence Manager and later scaled it as Principal Engineer. Dynamic SQS job generation and distributed workers supported 20,000+ routing workloads per quarter.
Python supported ArcPy map production, Pandas and psycopg2 data workflows, scheduler tuning, worker-cache cleanup, queue-depth balancing, and log-lifecycle controls. I also built release verification, rollback, health checks, container recovery, and infrastructure as code while participating in the production on-call rotation.
Routing workloads each quarter
Airflow orchestration and dynamic SQS job generation supported distributed national planning workloads.
Servers modernized
Modernization covered ESRI, routing, Python, ETL, and database environments.
AI engineering expansion
Models operate inside explicit system boundaries.
In my portfolio projects, models handle scoped generation, extraction, planning, critique, and ranking. Deterministic services retain identity, state transitions, calculations, schema checks, retries, and release gates. Human approval remains required for job submission and content publishing.
Application depth
Prompt workflows grew into applications and operating pipelines.
The combined implementation evidence across Apex Human Hybrid, Job Hunt OS, Level-Up Learning, and New Pencil Lead Intelligence includes FastAPI, Pydantic, PostgreSQL/PostGIS, React, Vue, a Swift wrapper, Google Apps Script, Playwright, containers, APIs, and background workers.
Provider-specific workflows use Codex, Claude, Gemini, and local models with structured outputs, cross-model review, fallbacks, deterministic validation, and human review. Each case study states its working scope and current limits.
Detailed project evidence
Review the architecture and operating decisions.
The case studies show how prompt experiments became data pipelines, APIs, interfaces, workers, and operated workflows. Each study covers the model's job, deterministic controls, failure handling, and current limits.