Reducing Time-to-Fill by 71% with AI-Powered Talent Intelligence

Challenge

A fast-growing mid-market SaaS company needed to scale its engineering team from 40 to 120 developers within 9 months to meet product roadmap commitments. Their internal recruiting team of 3 was overwhelmed — averaging 68 days to fill technical roles, with offer acceptance rates below 45%. Manual resume screening consumed over 20 hours per week, and passive candidates were being missed entirely. Two critical cloud infrastructure hires had been open for over 5 months, delaying a major platform migration. The company was losing top candidates to faster-moving competitors before they could even schedule first-round interviews.

Solution

Engineering Square deployed its AI-enhanced RPO model, embedding a managed recruiting team augmented by proprietary ML talent sourcing technology. Custom machine learning models were trained on the client's existing hire and performance data to score candidate fit across technical skills, career trajectory, and cultural alignment. The AI sourcing engine scanned millions of profiles across LinkedIn, GitHub, Stack Overflow, and academic databases — surfacing passive candidates that keyword-based searches missed entirely. Automated screening workflows handled resume parsing, initial qualification, and interview scheduling, while a recruitment intelligence dashboard gave leadership real-time visibility into pipeline health, bottleneck identification, and recruiter performance metrics. The system integrated directly with the client's Greenhouse ATS via API, requiring no platform migration.

Results

• 71% reduction in average time-to-fill — from 68 days to 19.5 days for technical roles
• 82 engineers hired in 9 months, exceeding the target of 80 and completing the scale-up ahead of schedule
• Offer acceptance rate improved from 45% to 78% through better candidate-role matching and faster process
• Recruiter screening workload reduced by 60% — freeing the internal team to focus on candidate experience
• 3.5x increase in qualified passive candidate pipeline through ML-powered sourcing across 4 platforms

Technologies