Automating Resume Screening with LLMs, Python, and FMC

With high volumes of job applications, manual resume screening becomes inefficient. The author proposes a solution for automating initial candidate filtering using LLMs, Python, and FMC (Foundation Model Cloud). The goal is not to replace recruiters, but to create a tool for technical data extraction that helps prioritize candidates. A key advantage is avoiding self-hosted MLOps, bypassing the complexities of GPU selection, CUDA configuration, and infrastructure maintenance. Instead, the author leverages an API that integrates seamlessly into existing workflows. This approach allows developers to focus on business logic while delegating resource-intensive text processing to cloud providers. The final hiring decision remains with the human, ensuring a balance between automation and the quality of competency assessment.