AI Talent Search & Resume Intelligence Platform

Sanitized professional case study based on enterprise recruitment/search platform experience. Client names, internal data, screenshots, and exact metrics are intentionally omitted; this describes the architecture and my role, not proprietary implementation details.

One-line summary

Semantic search and resume intelligence layered onto an enterprise recruitment platform — turning keyword matching into meaning-aware candidate–job matching.

Problem

Recruiters search large candidate pools with keyword filters that miss qualified people who phrase their experience differently. Resumes arrive in inconsistent formats, and the institutional context that makes a candidate a good fit isn't captured in structured fields.

Solution

An AI search 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. Embeddings + search — index candidate and job text into OpenSearch with hybrid (lexical + vector) retrieval so semantically similar candidates surface alongside exact matches.
3. Candidate–job matching — score and rank candidates against a role using retrieval-augmented prompts, with the reasoning grounded in the retrieved profile fields rather than generic output.

My role

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

Tools

OpenSearch (hybrid search), embedding models, RAG prompting, Python parsing/ETL microservices, .NET and Node.js platform services, and AWS for storage and compute.