Technical candidate certification system
Systems and methods to asynchronously vet, appraise, grade, rank and certify technical candidates for hiring are described. The technical candidate certification system administers and evaluates a set of specialized and customized evaluations of a technical candidate to generate a technical candidate ranking value, the ranking value typically provided relative to other technical candidates. The set of evaluations are customized to the job requirements of the client employer and to the profile of the candidate employee, and may be specialized to the hiring of software developer candidate employees. In one aspect, the technical candidate ranking system administers a software coding test and a technical interview, and provides an assessment and measure of a candidate's language proficiency. The client employer is provided with a ranking value of the recommended employee candidates.
1 . A system for certifying a candidate comprising:
a system database configured to: i) store a notional coding file comprising endpoints and software bugs; ii) store a candidate updated coding file and a candidate new coding file; iii) store a set of system scoring parameters comprising a candidate code execution time threshold value; iv) store a set of technical interviews comprising a set of technical interview questions; and v) store a candidate interview audio answer data;
a user interface configured to: i) provide the notional coding file to the candidate; ii) receive from the candidate the candidate new coding file and the candidate updated coding file, the notional coding file changed by the candidate to create the candidate updated coding file; iii) present to the candidate a particular technical interview; and iv) receive the candidate interview audio answer data;
a processor configured to: i) receive a set of candidate requirements; ii) retrieve the candidate new coding file and the candidate updated coding file from the system database; iii) retrieve the set of system scoring parameters from the system database; iv) record the candidate interview audio answer data; and v) record a candidate verbal comment data generated by the candidate while the candidate created the candidate updated coding file;
wherein the processor operates to:
compile and execute the candidate new coding files and the candidate updated coding file;
execute an end to end test on the candidate updated coding file and measure an execution time of the candidate new coding file;
transcribe the candidate verbal comment data into digitally transcribed candidate verbal comment data;
based on the end to end test, determine if the candidate updated coding file does or does not integrate with the endpoints of the notional coding file;
calculate a time difference between the execution time of the candidate new coding file and the candidate code execution time threshold value;
calculate a candidate coding score based on at least the time difference and whether the candidate updated coding file does or does not integrate with the endpoints of the notional coding file;
execute the technical interview to generate the candidate interview audio answer data;
digitally transcribe the candidate interview audio answer data into transcribed candidate interview answer data associated with the set of technical interview questions;
calculate, using at least one of an AI model and an LLM model, a candidate technical interview score based on the transcribed candidate interview answer data; and
generate a ranking score and a certification decision for the candidate based at least on the candidate coding score and the candidate technical interview score.
2 . The system of claim 1 , wherein the candidate technical interview score is established using at least both an AI model and an LLM model.
3 . The system of claim 2 , wherein the coding score is also at least based on a determination of the candidate updated coding file correcting the software bugs.
4 . The system of claim 1 , wherein the coding score is also at least based on a code assessment of at least one of code smells, code vulnerabilities, code reliability, and code linting.
5 . The system of claim 1 , wherein a code assessment is performed using an AI model.
6 . The system of claim 1 , wherein the transcribed candidate interview answer data is transformed into a set of vector representations.
7 . The system of claim 6 , wherein the set of vector representations are used as input into an AI model to establish the candidate technical interview score.
8 . The system of claim 1 , wherein the candidate coding score is also based at least on an evaluation of at least one of test code execution speed, test code security, and test code linting by one or both of an AI model and an LLM model.
9 . A method for certifying a candidate comprising:
providing a system database configured to: i) store a notional coding file comprising endpoints and software bugs; ii) store a candidate updated coding file and a candidate new coding file; iii) store a set of system scoring parameters comprising a candidate code execution time threshold value; iv) store a set of technical interviews comprising a set of technical interview questions; and v) store a candidate interview audio answer data;
providing a user interface configured to: i) provide the notional coding file to the candidate; ii) receive from the candidate the candidate new coding file and the candidate updated coding file, the notional coding file changed by the candidate to create the candidate updated coding file; iii) present to the candidate a particular technical interview; and iv) receive the candidate interview audio answer data;
providing a processor configured to: i) receive a set of candidate requirements; ii) retrieve the candidate new coding file and the candidate updated coding file from the system database; iii) retrieve the set of system scoring parameters from the system database; iv) record the candidate interview audio answer data; and v) record a candidate verbal comment data generated by the candidate while the candidate created the candidate updated coding file;
wherein the processor operates to:
compile and execute the candidate new coding files and the candidate updated coding file;
execute an end to end test on the candidate updated coding file and measure an execution time of the candidate new coding file;
transcribe the candidate verbal comment data into digitally transcribed candidate verbal comment data;
based on the end to end test, determine if the candidate updated coding file does or does not integrate with the endpoints of the notional coding file;
calculate a time difference between the execution time of the candidate new coding file and the candidate code execution time threshold value;
calculate a candidate coding score based on at least the time difference and whether the candidate updated coding file does or does not integrate with the endpoints of the notional coding file;
execute the technical interview to generate the candidate interview audio answer data;
digitally transcribe the candidate interview audio answer data into transcribed candidate interview answer data associated with the set of technical interview questions;
calculate, using at least one of an AI model and an LLM model, a candidate technical interview score based on the transcribed candidate interview answer data; and
generate a ranking score and a certification decision for the candidate based at least on the candidate coding score and the candidate technical interview score.
10 . The method of claim 9 , wherein the candidate technical interview score is established using at least both an AI model and an LLM model.
11 . The method of claim 10 , wherein the coding score is also at least based on a determination of the candidate updated coding file correcting the software bugs.
12 . The method of claim 9 , the coding score is also at least based on a code assessment of at least one of code smells, code vulnerabilities, code reliability, and code linting.
13 . The method of claim 9 , wherein a code assessment is performed using an AI model.
14 . The method of claim 9 , wherein the transcribed candidate interview answer data is transformed into a set of vector representations.
15 . The method of claim 14 , wherein the set of vector representations are used as input into an AI model to establish the candidate technical interview score.
16 . The method of claim 9 , wherein the candidate coding score is also based at least on an evaluation of at least one of test code execution speed, test code security, and test code linting by one or both of an AI model and an LLM model.
17 . A system for certifying a candidate comprising:
a system database configured to: i) store a notional coding file comprising endpoints; ii) store a candidate updated coding file and a candidate new coding file; iii) store a set of technical interviews comprising a set of technical interview questions; and iv) store a candidate interview audio answer data;
a user interface configured to: i) provide the notional coding file to the candidate; ii) receive from the candidate the candidate new coding file and the candidate updated coding file, the notional coding file changed by the candidate to create the candidate updated coding file; iii) administer to the candidate a particular technical interview; and iv) receive the candidate interview audio answer data;
a processor configured to: i) receive a set of candidate requirements; ii) retrieve the candidate new coding file and the candidate updated coding file from the system database; iii) record the candidate interview audio answer data; and iv) record a candidate verbal comment data generated by the candidate while the candidate created the candidate updated coding file;
wherein the processor operates to:
compile and execute the candidate new coding files and the candidate updated coding file;
execute an end to end test on the candidate updated coding file and measure an execution time of the candidate new coding file;
transcribe the candidate verbal comment data into digitally transcribed candidate verbal comment data;
based on the end to end test, determine if the candidate updated coding file does or does not integrate with the endpoints of the notional coding file;
calculate a candidate coding score based on whether the candidate updated coding file does or does not integrate with the endpoints of the notional coding file;
execute the technical interview to generate the candidate interview audio answer data;
digitally transcribe the candidate interview audio answer data into transcribed candidate interview answer data associated with the set of technical interview questions;
calculate, using at least one of an AI model and an LLM model, a candidate technical interview score based on the transcribed candidate interview answer data; and
generate a ranking score and a certification decision for the candidate based at least on the candidate coding score and the candidate technical interview score.
18 . The system of claim 17 , wherein the candidate technical interview score is established using at least both an AI model and an LLM model.
19 . The system of claim 17 , wherein the coding score is also at least based on a code assessment of at least one of code smells, code vulnerabilities, code reliability, and code linting performed by an AI engine.
20 . The system of claim 17 , wherein the candidate coding score is also based at least on an evaluation of at least one of test code execution speed, test code security, and test code linting by one or both of an AI model and an LLM model.