Precision Hiring at Scale: Vector Search + Filters for HR Marketplaces

Upgrade classic keyword job search, power resume-based recommendations, and deliver "similar jobs" experiences using a Vector + Filter architecture designed for real-world constraints.

Advanced filters for precision hiring

Real-time matching speed

Scale without the cost spike

Talk to an Expert

HR Tech illustration with hiring dashboard and candidate matching

Pariti used Qdrant's vector search capabilities to rank candidates, increasing the hiring fill rate from 20% to 48%, and decreasing candidate vetting time from 4 minutes to 1 minute, a 70% time-savings.

"Qdrant is the last thing I worry about breaking."

Elvis Moraa

Engineering Lead, Pariti

The problem: recruitment data is fuzzy, but hiring constraints are strict

Recruitment search and matching must handle ambiguity (skills synonyms, inconsistent job titles, incomplete resumes), and enforce hard constraints (location, work authorisation, certifications, salary bands, categories).

"Semantic match" alone is insufficient if the candidate or job fails mandatory filters, and keyword-only search misses great matches when terminology doesn't line up. Additionally, job application numbers are increasing due to AI.

Diagram showing recruitment data vs hiring constraints mismatch

What you can build with Qdrant

Flow chart showing parse, search, rank, and match process

Modernize classic job search

Keep existing Boolean/keyword patterns where needed while adding semantic relevance with hybrid search.

Apply strict filters alongside semantic search (Vector + Filter) for precise hiring constraints.

Maintain performance as data grows and traffic spikes.

Resume-based job matching

What: users upload a CV and get job recommendations with no manual query input.

Why retrieval matters: CVs are messy (titles and skills rarely match cleanly) so semantic similarity improves match quality and diversity while filters keep results valid.

Why Qdrant for HR Tech

Advanced Filters for Precision Hiring

With Qdrant, strict filters can be applied alongside semantic search without sacrificing performance, enabling real hiring workflows (location, certifications, categories, salary bands).

Engineered for Real-Time Matching & Speed

In recruitment, speed is a competitive advantage. Qdrant's architecture is built to keep ranking responsive and reduce the "latency spike" behavior teams encounter in legacy setups.

Scalability Without Cost Spikes

Scale from millions to tens of millions of vectors with cost-conscious optimization options (e.g., quantization) while maintaining throughput.

Handling Fuzziness by Design

Job titles and skill taxonomies are inconsistent, but Qdrant surfaces strong matches even when terminology doesn't line up with semantic similarity and recommendations.