Dino Meschini
I build backend and AI-agent systems in Go and Python on Azure.
One example is a provenance-enforced equity-analysis pipeline over SEC EDGAR/XBRL filings, with typed review gates, fail-closed required inputs, and live end-to-end validation on AAPL, MRNA, and UBER.
Open to backend roles — remote, Argentina-based, EU work authorization (Italian citizenship).
Projects
3 shipped, soloMulti-agent LLM pipeline over SEC EDGAR/XBRL filings, with cross-model review of every generated analysis.
WhatsApp-native intake agent in Go; LLM-based triage ran live with real users before the product was wound down.
Full product — Go/PostgreSQL backend, React/Vite front end, Claude-assisted document processing — built and operated on OCI end to end.
Experience
2 selected rolesLed the migration of legacy microservices from JavaScript to Go and built gRPC APIs backed by PostgreSQL and MongoDB.
Automated onboarding operations with Python scripts and API integrations for reliable data exchange.
Engineering approach
provenance
Every number a system emits should be traceable to its source. If you can't audit it, you can't ship it.
end-to-end ownership
From schema to deploy to the invoice for the infra. Three products taught me what "done" actually costs.
direct communication
Plain statements of what works, what doesn't, and what it will take. No marketing energy.