AI and data systems that repeat exactly, show their work, and ship. 15+ years turning messy data into production systems — from large-scale agricultural data pipelines at Bayer / The Climate Corporation, to Wear OS apps that pair straight into your own AI gateway — no phone, no cloud relay — to self-hosted stacks I build and run today, and teach you to deploy and own for yourself.
Nine lanes, one theme: systems whose outputs a peer can recheck line by line.
Deterministic agent architectures, LLM integration, and RAG/skill layers that repeat exactly and show their reasoning — not demo-ware.
Python · LLM · RAGAg is home ground, not a side lane: research-trial pipelines, machine telemetry ingest (CAN → ISO-XML / ISO 11783), weather, imagery, and field-boundary fusion — with the crop-science background to validate the agronomy, not just move the bytes. Fifteen years inside Monsanto, The Climate Corporation, and Bayer digital-farm data teams is the credential: I worked on the big platforms, so I know what holds up in a field and what does not. My Farm Advisor skills are the separate, open-source mission: field data has always been hard to get for everyone — datasets found one by one, formats deciphered by hand. The goal is to make that access as easy as humanly possible, for any grower, anywhere.
AgTech · ISO-XML · Research trialsNative Android and standalone Wear OS development: health & sensor services, complications and notifications, background sync, and buttery UI at 1.2-inch scale. Comfortable across the full app lifecycle — Play Store or private distribution.
Kotlin · Wear OS · Health & sensorsBatch and scripted ETL/ELT across raster, vector, tabular, and time-series data. The core strength: feature engineering that survives peer review — and model training, inference, and serving that scale without surprises.
Python · SQL · PySparkWeb, API, and product surfaces end to end — TypeScript front ends, Python back ends, data model and deploy included. One person from schema to shipped.
TypeScript · Python · APIsI don't just hand over a system and walk away — I teach you to run and extend it. Enablement is scoped to your stack and team: embedded sessions, code walkthroughs, and custom curriculum so you own what we built together. My ELVTR agricultural data course — farm-advisor skills plus deploying your own stack — is the public proof I can teach; private engagements cover any domain.
Workshops · Curriculum · FDE-styleYour stack on your hardware: Docker, Cloudflare Tunnel/Access, TLS, one-command deployments — and the documentation to hand it over. Including local AI inference: LLM, image, video, and audio generation tuned to your GPU, from consumer cards to workstation silicon.
Docker · Linux · Cloudflare · Local AICharts and dashboards that turn any dataset into decisions: executive KPIs, deep-dive time series, maps and spatial layers — whatever the domain, whatever the scale. Static plots that print crisp, and interactive views that answer the second question.
Dashboards · Charts · Data storytellingDeterministic pipelines end to end: PR preview environments, credential-free deploy automation, everything-as-code in git, one-command bootstrap from bare metal to production. The pipeline that deploys this page is a working example — repo is private; read access on request.
GitHub Actions · Preview envs · GitOpsEverything I pitch, you can read line by line. Three repos I stand behind:
Python-first installer that stands up a full self-hosted stack on a fresh Ubuntu VPS: Dokploy, Cloudflare Tunnel, Tailscale, and LiteLLM — one AI key setup shared by every app. Ships pre-made Coder workspaces.
Standalone Wear OS client for the self-hosted Hermes AI gateway: 6-digit-PIN pairing, then voice/text chat with your agent over a private WireGuard tailnet — streamed markdown/mermaid, on-device TTS, run cancel, session resume, body tools. No phone app, no cloud.
Domain-specific skills for the My Farm Advisor agent ecosystem: portable, deterministic skill packs that turn farm, field, and trial data into analyses a peer can recheck line by line.
Fifteen years turning messy data into systems that ship — most recently as founder and instructor, before that inside one of agriculture's largest data platforms.
Independent consulting practice: deterministic agent pipelines and data engineering. Custom Python/Kotlin systems, self-hosted client stacks (Dokploy, Docker, Cloudflare), one-command deployments, and documentation-first handover.
Founded and operate My Farm Advisor — an agricultural data platform with a deterministic agent layer: portable skill packs that turn farm, field, and trial data into analyses a peer can recheck line by line. Full stack: Python/SQL ingest, geospatial pipelines, self-hosted Ubuntu deployments. myfarmadvisor.com ↗
Author and instructor of a hands-on course on building practical AI agents for agriculture: from data pipelines to LLM integration, taught to a professional audience on the ELVTR platform. View the course ↗
Built and operated large-scale ETL/ELT pipelines feeding weather, field, and imagery data into digital-farm products. Scaled feature generation across raw, derived, and engineered features; extended the model-validation framework to tie business metrics to model performance; designed geospatial ingest (CAN bus telemetry, satellite imagery, SSURGO) and self-service analytics tooling.
Verified details and working history also on my Upwork profile ↗.
Contract work, technical co-founder and engineering-lead conversations at small high-impact companies, strategic advising, architecture reviews, agent-pipeline rescue, or a course on doing this yourself — tell me what you're trying to build or own, and I'll tell you honestly whether I'm the right fit.
Also on LinkedIn ↗ — full background, ventures, and what I'm open to next.