Anix Lynch
Anix Lynch
AI Data Engineer · GenAI Architect
Los Angeles, CA
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About

Most AI projects fail before the model. I build the systems that connect trusted data, meaningful signals, AI workflows, and human adoption — Truth → Context → Signals → Actions → Adoption.

Portfolio

Production AI systems — real evals, real costs, traceable to code.

All 9 projects
All 9 GenAI 3 Data Eng 2 AI Platform 3 Automation 1 Cloud 7 Solutions 1
Featured Projects
Healthcare AI Signal Engine
AI Platform ☁ Vertex Live

Healthcare AI Signal Engine

Gives Gemini both trusted facts and computed signals, then shows how the decision changes.

PythonFastAPIVertex AI +7
⚡ 5 ms Near-instant answers
Healthcare AI Data Engineer
Data Eng ☁ Vertex Live

Healthcare AI Data Engineer

A data backbone that's actually deployed — dbt medallion on BigQuery (55 tests green), a quality gate that caught 5,500 bad rows, and an AI agent that only answers from the trusted marts.

PythondbtBigQuery +9
🗄️ 55,500 Rows live in BigQuery
Healthcare GenAI Evidence Engine
GenAI ☁ Vertex Live

Healthcare GenAI Evidence Engine

Answers clinical questions with records, citations, privacy checks, and a release gate.

PythonFastAPIBM25 +8
⚡ 50 ms Hybrid p95 latency
Pied Piper Legal Simulator
GenAI ☁ Vertex Live

Pied Piper Legal Simulator

Read a term sheet like a senior analyst — Gemini cites the clause, scores who it favors, and names the Silicon Valley episode it mirrors.

PythonFastAPIVertex AI +4
📜 57 Clauses grounded
Healthcare Forward Deployed Engineer
Solutions ☁ Vertex Live

Healthcare Forward Deployed Engineer

An ER triage assistant a customer can actually deploy — graded by acceptance contracts, not ML metrics. If a suicidal patient doesn't get human review, it isn't done.

PythonFastAPIBM25 +8
🤝 10 Customer-contract tests
Healthcare Analytics on Microsoft Fabric
Data Eng ☁ Azure

Healthcare Analytics on Microsoft Fabric

Power BI you can code-review — dbt + Fabric lakehouse + TMDL in git, where every claim maps to a file.

Microsoft FabricPower BIdbt +6
🏥 55.5K Encounters modeled
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