Geospatial data & AI engineer · Tampa Bay, FL
Geospatial data.Operational analytics.Applied ML & AI.
I turn scattered spatial and operational data into systems people can make decisions from: ingestion and ETL, warehouses and reporting layers, spatial analysis, and the ML and LLM features on top. Built end to end, validated, and documented so people who aren't specialists can use them.
From disparate sources to numbers people can trust.
Most of the hard part is upstream: sources with no shared identifier, geographies that don't line up, APIs with result ceilings, and pipelines that report success while the data quietly goes stale. I build the layer that makes that data trustworthy, then the analysis, dashboards, and models on top.
Recently: a nationwide geospatial data engine for Harvard Business School research, a revenue operations warehouse on GCP for Mawer Capital, 538andMe.io (a civic-data platform with grounded Q&A over bill text), and a QGIS plugin that puts the Google Earth Engine catalog inside desktop GIS.
Geospatial first, then the data and AI layers around it.
Geospatial Data & Analysis
Spatial ETL, PostGIS, Earth Engine and QGIS tooling, and interpolation across geographies that do not line up.
Data Engineering & Ops Analytics
Warehouses, API ingestion, and reporting layers, with health checks that track freshness, not just job success.
RAG & Knowledge Retrieval
Hybrid lexical and vector retrieval in Postgres, local embeddings, and answers that cite their source.
MLOps & Model Deployment
Cost and latency tracking, evaluation on real examples, human review, and deploys that can be rerun safely.
Agentic AI & LLM Automation
Agents with tight limits: Claude skills, validated configs, least-privilege access, and a human sign-off.
Platform Engineering
FastAPI services, schema as code, and admin tooling that data and AI features sit on.
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Hiring for geospatial, data, or AI work?
Open to geospatial, data engineering, analytics, and ML/AI roles, including solutions architecture. Tampa Bay, remote-ready.