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AI-Powered Job Matching Engine Support

Active
Federal

Contract Overview

Solicitation details, issuing organization, response deadlines, documents, and interested companies for this government contract opportunity.

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This contract involves supporting the development and refinement of AI and machine learning algorithms designed to match student profiles with federal job opportunities listed on USAJOBS. The scope includes not only the training of models but also implementing bias mitigation strategies to ensure fairness and accuracy in job matching. The work is subcontracted under the NAICS code 541511, which pertains to custom computer programming services. The contract is managed by the Office of Centralized Acquisition Services within the General Services Administration, with the place of performance specified as Washington, DC (zip code 20415). Although certain details such as solicitation number and set-aside status are not provided, the focus is clearly on enhancing a job matching engine powered by AI to better align educational backgrounds with federal employment openings, streamlining the hiring process for students seeking government positions.

General Info

Support development of AI algorithms matching student profiles with federal jobs, ensuring fairness.

Agency

General Services Administration → Office Of Centralized Acquisition ServicesView Agency

NAICS

541511 - Custom Computer Programming ServicesView NAICS

Place of Performance

Washington, DC, 20415, USA

Set-Aside

NONE

Documents

(0)

No documents available

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Timeline

Posted

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Organization & Contact Information

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AgencyGeneral Services Administration → Office Of Centralized Acquisition Services
ContactsNo contacts available
OfficeN/A
Organization / Agency
General Services Administration → Office Of Centralized Acquisition Services
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Office AddressN/A
ContactsNo contact information available

Full Description

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Support development and tuning of AI/ML algorithms that match student profiles with federal job openings on USAJOBS, including model training and bias mitigation.

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