Building AI for healthcare leaves zero room for error.
I’m currently collaborating with an incredible team on Raphald AI, a medical detection application. Building the systems for a project with stakes this high is a massive reminder that the underlying backend architecture matters just as much as the machine learning model itself.
When integrating diagnostic AI, your API endpoints cannot drop requests, and your database workflows demand absolute integrity. You aren't just passing JSON payloads; you are handling critical, real-time workflows where stability is non-negotiable.
Engineering these systems continues to shape my approach to building robust Python backends. If you are developing a product that requires reliable AI integration or rock-solid FastAPI infrastructure, check out the newly updated services on my profile. Let's build something that works when it counts.
B2 English, based in Argentina. Payment preferably in USDT. Remote only. Quite a combination, right?
I built an AI-assisted job-search system around Freehire and Hirify. Both platforms offer official API/CLI access for automation, which makes them much easier to connect to an agent workflow. Simple Python scripts collect job listings and run the first pass of filtering against strict rules.
Then agents double-check the requirements, match them against my experience, and tailor my CV to each role. They highlight relevant projects and technologies I've actually worked with. Final review and approval stay with me.
𝐀𝐬 𝐨𝐟 𝐒𝐞𝐩𝐭𝐞𝐦𝐛𝐞𝐫 𝟐𝟔:
✅ 20 applications sent;
✅ 8 HR screenings passed;
✅ 1 video screening completed and submitted.
🚀 Technical interviews are still ahead.
I'm not ready to write off the market. Even with my constraints, things are moving.
A job search deserves 𝐚𝐧 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐚𝐩𝐩𝐫𝐨𝐚𝐜𝐡 𝐭𝐨𝐨: clear rules, automation, and checks on the results.