Autonomous AI Recruiting & Candidate Sourcing Pipeline
End-to-end agentic recruiting and talent sourcing pipeline automating candidate discovery, profile enrichment, semantic resume matching, and personalized outreach.
Business Challenge
Talent acquisition teams spend disproportionate manual hours sourcing passive candidates across fragmented platforms, screening high-volume resumes against nuanced technical job descriptions, and drafting personalized outreach—leading to slow time-to-hire and inconsistent candidate evaluation.
Solution Delivered
Architected and deployed an automated AI recruiting and sourcing pipeline using Python, LLM agents, vector embeddings, and workflow orchestration. The system ingests role specifications to generate structured search queries, enriches candidate profiles, performs semantic scoring and rubric-based technical evaluation against job requirements, and triggers personalized multi-stage outreach sequences with human-in-the-loop approval gates. Repository: https://github.com/parmarjh/ai-recruiting-sourcing-pipeline
Outcomes Achieved
Automated over 80% of top-of-funnel candidate sourcing, profile enrichment, and initial technical screening workflows, reducing manual resume review and shortlisting time from hours per role to minutes while maintaining structured rubric scoring.
Measurable Business Outcome
Automated 80%+ of top-of-funnel candidate sourcing and screening; reduced manual resume review and shortlisting time from hours per role to minutes.