3x
faster chat replies on the support agent. A 45-second wait ran on every message. Now it's 15 seconds mid-conversation.
Most AI agents look great in a demo and fall apart with real customers. I'm the engineer who finds out why and fixes it, with numbers to prove the fix worked.
13 of 23
test questions now find a document the agent used to miss. The right one ranks 1st or 2nd instead of 3rd.
The dashboard number swung 4x in three weeks while the bot hadn't changed at all. I replaced it with a metric that counts real resolutions: no escalation, no reopen within 7 days, no human reply.
3x
faster chat replies on the support agent. A 45-second wait ran on every message. Now it's 15 seconds mid-conversation.
60→5
minutes for a credit analysis: about an hour before, as little as 5 with my pipeline over 5+ credit bureaus.
10→100
credit operations a day: about 10 before, up to 100 once the heavy work moved to RabbitMQ and Celery workers.
0.0205
MAE, the best result of my research, from plain DES on visceral leishmaniasis with HIV. No single model won everywhere.
It answers customers on WhatsApp, web chat and email. I shipped about 30 releases in 4 weeks, and replies got 3x faster.
I rebuilt a credit fund's manual credit analysis as Python services with queues and workers.
My first Go project. A sidecar agent streams logs over gRPC to a live dashboard, and healthmon blocks new requests to an external API once its error rate crosses a threshold.
A PDF notes app and a resume generator on ChromaDB, plus Kai, my personal copilot, with embeddings and cosine similarity in numpy. The bug I remember: every restart wiped the vector index.
Scrapers for Brazil's public health database feed a dashboard and a forecasting study. No single model won everywhere.
I'm open to remote backend and AI engineering roles with US and European teams. I also take small freelance projects through my company, Insight AI & Automation.