REVIEW BRIEF

Big Tech Systems Engineering Proficiency

Doeon Kim — Full-stack systems design across data, AI, and platform engineering

Systems at Scale

Demonstrated ability to design, build, and ship production systems across the full stack.

System	Scale / Complexity
stage-pilot (npm)	Published package, 1,720 tests, streaming-first architecture
AegisOps	Multimodal pipeline: image + text analysis, live deployment
Nexus-Hive	Multi-agent graph with governed SQL across 2+ warehouses
Lakehouse Contract Lab	3-tier medallion pipeline with cross-platform export
DistrictPilot AI	End-to-end native platform app: ML + AI + UI in one runtime

Engineering Principles

Testing discipline — 1,720 tests across unit, integration, and benchmark suites for stage-pilot.
Observability — Datadog certified; metrics, traces, and log correlation for production AI workloads.
Data governance — RBAC, PII redaction, audit logging, data contracts, schema enforcement.
API design — Published npm package with streaming-first, type-safe interfaces.
Multi-language — TypeScript, Python, SQL, Spark — choosing the right tool for each layer.

Technical Breadth

Domain	Demonstrated Skills
AI/ML	QLoRA fine-tuning, LangGraph agents, tool-calling, multimodal
Data Platform	Snowflake, Databricks, Spark, Delta Lake, medallion architecture
Backend	FastAPI, Node.js, streaming parsers, REST/GraphQL APIs
Frontend	React, Streamlit, TypeScript SPAs
DevOps	CI/CD, Docker, npm publishing, automated testing

Certifications

SnowPro Associate
Databricks Certified Platform Architect
Palantir Foundry Data Engineer + Foundations
Datadog Observability Fundamentals
Microsoft AI-900

Key Differentiator

Rare combination of AI research depth (fine-tuning, agents, evals) with production engineering rigor (1,720 tests, published packages, data contracts) and multi-platform fluency (Snowflake, Databricks, Palantir).

stage-pilot Full Portfolio