Recruiters wake up to find hundreds of resumes in their recruiting systems. The current filtering systems were created in the 1990s and do not give a holistic profile of a candidate, leaving little wonder that many people consider technical recruiting to be broken. We have used various Machine Learning algorithms and the UiPath framework to build this project.

What it does

Our system accepts a resume and uses UIPath's framework to automate extraction of links from it. The resume is parsed in the meantime for keywords which are presented to a recruiter that can review the results and enter keywords for the role or about the candidate they are looking for or looking to hire for. The product then presents the top 5 candidates within the system, from which the recruiter can select a candidate to view their comprehensive profile, presented on a dashboard.

How I built it

Flask backend, Angular/Bootstrap frontend, Google Cloud, Python for Machine Learning

Challenges I ran into

Distributed Scale, Differing dependencies across systems, IAM access roles,

Accomplishments that I'm proud of

What I learned

What's next for Automated Candidate Evaluation (ACE)

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