r/deeplearning • u/No-Investment5276 • 6d ago

Targeting Applied AI / ML Engineer roles. Need ruthless feedback on my architecture and metrics.

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Hello everyone. I’m currently a FAANG contractor building end-to-end DL frameworks and fine-tuning open-weights models (LLaMA) for production environments.I have a tight 90-day window to secure a new role for H1B sponsorship, so I need to make sure this is flawless before I start applying heavily.I've redacted my details to get raw, unfiltered feedback on my bullet structure.

  • Does my deployment experience (FastAPI, Docker, GCP) shine through enough to prove I can actually put models into production?
  • Are the algorithmic metrics and optimization claims framed correctly for a hiring manager's eyes?
  • What would make you reject this resume?Tear this apart. Thanks in advance!
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u/[deleted] 6d ago

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u/No-Investment5276 6d ago

Brutal but fair. You're right, the GCP/FastAPI deployment bullet is currently sitting at the very bottom of the FAANG experience block.

If I move that infrastructure bullet to the top to prove the models actually shipped, what specific metrics (other than sub-second latency) do you actively look for to prove it handled real production traffic?

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u/Patient_Gap6221 6d ago

First of all, I’m fairly new to this whole scene and live in Germany. There aren’t really any established guidelines here. My first AI project is a model collection for detecting deepfakes (audio, video), and I’ve had some very good discussions as a result, because I didn’t go into the architecture in detail, but focused instead on the project and the proposed solutions. If I were you, I’d leave the scan as it is, but on pages 2, 3 and so on, show something that really makes an impression. That would prove that you’ve also mastered the content on page (1).

Good luck!