AI Talent Search & Resume Intelligence Platform

Sanitized professional case study based on enterprise recruitment/platform experience. Client names, internal data, screenshots, credentials, and exact metrics are intentionally omitted; this page describes public-safe architecture, responsibilities, and delivery patterns.

One-line summary

Search and resume intelligence layer that helps recruiters find relevant candidates even when resumes and job requirements use different wording.

Problem

Recruiters search large candidate pools with keyword filters that miss qualified people who describe their experience differently. Resumes arrive in inconsistent formats, and the context that makes someone a good fit for a role rarely lives in clean, structured fields.

Solution

A search and resume intelligence layer on top of the existing recruitment platform:

1. Resume intelligence — parse and normalize resumes into structured candidate profiles, extracting skills, roles, and experience.
2. Search — index candidate and job text with hybrid (keyword + vector) retrieval, so closely related candidates show up alongside exact matches.
3. Candidate–job matching — score and rank candidates against a role, keeping the ranking tied to the candidate's actual profile fields rather than generic model output.

My role

Solution architecture and delivery: search and matching design, data and indexing patterns, integration with the recruitment platform's existing .NET/Angular services, and guiding the parsing and ranking workflows.

Tools

OpenSearch and Apache Solr for search, embedding models, RAG for grounded ranking, Python parsing/ETL services, .NET and Angular platform services, and AWS for storage and compute.

Intentionally omitted

Client names, candidate data, internal models, screenshots, credentials, and production metrics. The relevance and matching behavior is described at an architectural level only.