An IT company with large software development operations has multiple simultaneous software projects. Some projects are owned and fully run by company itself while in most of other cases it develops software for its customers. Business is constantly growing as projects' scale and number is increasing. This growth along with regular temporary labor needs of specific projects is supported by internal HR team.

Due to fast growth of IT industry market dictates large and small companies must compete for talents and constantly sharpen their hiring process. Besides competition, hiring in IT has higher requirements to recruiters who also must demonstrate enough of IT knowledge to be able to evaluate applicant's CV and support screening interview to detect and disqualify unskilled applicants.

In most cases proper candidate evaluation requires professional analysis of sample computer code provided by candidate. This step requires expensive time of senior developer and there may be hundreds of code samples. It means that resolving shortage of software developers requires more hours of software developers for candidate evaluation. It's a vicious circle.

VisionSystems built a complex software solution that automatically evaluates job applications and computer code samples. Job applications and computer code samples are uploaded to customers' job portal (optionally link to GitHub). Recruiter accessing applications sees application and computer code rating next to each application with ability to filter out candidates not showing level of skill required for the position.

Recruiters' performance was increased by 2+ times as big part of applications was filtered out after automatic evaluation. The requirements to recruiters' skills and experience were also decreased since hiring process became less dependent on recruiter's skills. Number of computer code samples evaluated by senior developers has decreased by 7-10 times as only samples of automatically pre-qualified candidates required verification by human.

The core of AI the system is a software that uses machine-learning algorithms. We used large amount of historical data in combination with artificially synthesized samples to train ML models to rate candidate CVs and sample computer code. At the first stage system supports few popular programming languages: PHP, JavaScript, Laravel

Once job application is uploaded it is analyzed by machine learning model. Assessment result is returned and stored in the database with the application allowing recruiter to filter and sort candidates in accordance with their score.

This solution can evolve in multiple directions. The first obvious way to increase solution efficiency is adding more programming languages. Another way is to train system to evaluate UI/UX designers' portfolios or include candidates state of mind and emotional condition basing at the live interview.