Zac Plischka. Data Sci & AI Eng
I build production agentic AI — and the substrate beneath the model: the ontologies, governed provenance, and knowledge graphs that make AI memory trustworthy.
About
Snowflake SnowPro Core certified data scientist building production-grade ML systems, loyalty-program analytics, and agentic AI workflows.
The thesis: the substrate beneath the model. A vector database stores facts — it cannot tell you which version is true. I build the ontologies, governed provenance, and guardrails that make AI memory trustworthy.
At Xephyr, consulting for Wesfarmers Health, I architected and shipped Clarence — a two-tier agent knowledge platform serving 12 client tenants over MCP — built supplier campaign measurement frameworks projected to unlock $5M+ in annual sales lift, and built a text-to-SQL business-decision agent with concept resolution and an LLM-as-a-judge eval harness.
Before that, Data Engineer at Lightbulb Partners — migrated 200GB+ datasets to Snowflake and deployed RAG pipelines that cut manual fault-detection review time by 60%. Alongside all of it: four years as a disability support worker — the NDIS domain depth I bring to client work.
I work across the stack: text-to-SQL on Snowflake, GPU-native enterprise AI POCs on Nvidia H100s with LangGraph and vLLM, Power BI dashboards, FastAPI services, and the eval harnesses that make any of it shippable.
→ exit 0
- 2014 init: b2b sales — WHC machinery (~8 yr, concurrent)
- 2017 feat: BSc — monash
- 2021 feat: disability support — interchange outer east (4 yr)
- 2022 feat: master of data science — monash (concurrent)
- 2023 feat: data engineer — lightbulb partners
- 2025 feat: data scientist / ai engineer — xephyr
- 2025 ship: clarence → production · 12 tenants
- 2026 tag: snowpro-core · COF-C03 · verify ↗
- HEAD shipping production agentic AI — xephyr
[languages]
- python
- sql
- typescript
- javascript
- bash
[data_and_ml]
- RAG
- knowledge graphs
- hybrid vector search
- LLM-as-a-judge evaluation
- synthetic control · a/b testing
- customer segmentation (RFM)
- ontology design
[platforms]
- snowflake + cortex
- aws bedrock
- postgresql + pgvector
- graphiti + falkordb
- qdrant
- terraform · docker compose
- github actions
- power bi · fastapi · redis
[agent_infrastructure]
- mcp servers (http + stdio)
- claude agent sdk · skills
- headless claude orchestration
- litellm → bedrock routing
- fail-closed auth gateways
- architecture decision records
- ports-and-adapters DI
Selected work
Clarence ★
Two-tier agent knowledge platform — pgvector + Graphiti/FalkorDB — serving 12 client tenants over MCP. Fail-closed Agent-Key gateway, strict tenant isolation, one-command Terraform deploys.
Querus ★
Text-to-SQL business-decision agent — Qdrant concept resolution maps vague business terms to canonical entities before it writes SQL, with multi-environment warehouse routing and an LLM-as-a-judge harness scoring queries on semantic equivalence.
Tense
Temporal memory for AI agents — an MCP server storing knowledge as a bi-temporal graph on Postgres, answering which version is true now or as of any past date. 100% vs 0% against a fair vector baseline on point-in-time questions.
MyPickle
Pickleball court directory for Victoria, with an interactive map and crowd-sourced ratings. Auth-gated reviews and court submissions.
Dinder
Real-time collaborative restaurant decision app. Socket.IO over Redis TTL for ephemeral 30-minute group sessions. Built TDD with Vitest + Playwright.
Process
→ the system behind the trace: clarence case study