Introduction
Folens is one of Ireland's leading educational publishers. When they decided to build a digital product team based in India, the challenge was clear: find a lead full stack engineer, fast, with no dedicated recruiting support.
Sharans, Head of Product at Folens, was running the search himself. He needed someone strong in React, Python, and capable of designing scalable systems from scratch. This was a founding engineer hire. Getting it wrong would set the entire initiative back by months.
Screening 2,000+ applications and running technical interviews takes time that a product leader building a new team does not have. Folens used Fabric to handle the full top-of-funnel process.
Why is hiring engineers so difficult without a dedicated recruiting team?
The person who best understands what the role needs is usually the person least available to run a hiring process.
For Folens, this was Sharans. He knew exactly what technical skills mattered and what kind of engineer would thrive on a founding team. But he also had a product to build. Spending 50+ hours screening resumes and conducting first-round interviews was time he could not afford.
The volume makes it worse. A full stack engineer posting in India attracts thousands of applications. The gap between a resume that lists React and Python and a candidate who can actually architect a scalable system only becomes visible during a technical interview.
There is also the cheating problem. <a href="https://www.fabrichq.ai/blogs/state-of-ai-interview-cheating-in-2026-insights-from-19-368-interviews">Fabric's analysis of 19,368 interviews</a> found that 48% of technical candidates show cheating behavior. For a product leader screening without recruiting support, catching AI-assisted responses adds another layer of complexity.
What should you test when hiring a lead full stack engineer?
A founding engineer hire is different from filling a mid-level position on an established team. You need someone who can build, not just contribute.
1. Frontend and backend proficiency
For Folens, this meant React and Python. But proficiency goes beyond listing frameworks on a resume. A lead engineer should write clean, production-quality code and explain their decisions under pressure. Live coding exercises reveal this far better than take-home assignments.
2. System design ability
A founding engineer makes architectural decisions that the team lives with for years. Can they design systems that scale? Do they think about failure modes, data modeling, and service boundaries? System design conversations expose whether a candidate thinks at the level you need.
3. Professional experience and ownership
On a founding team, there is no one to hand off problems to. You want engineers who have owned projects end-to-end, made tradeoffs with incomplete information, and shipped under constraints.
How did Folens structure the AI interview?
Fabric ran a single 50-minute interview per candidate that covered all three areas. This replaced what would typically be two to three separate interview rounds.
1. Coding question
Candidates worked through a coding problem live. Fabric evaluated their approach, code quality, and how they handled edge cases. This tested practical engineering ability rather than memorized algorithm solutions.
2. System design
Candidates were asked to design a system relevant to the kind of work Folens would need. Fabric's AI asked follow-up questions about scalability, tradeoffs, and failure handling. Candidates who gave surface-level answers were pressed for specifics.
3. Professional experience deep-dive
Fabric explored candidates' past roles with targeted follow-ups. How did they make architectural decisions? What did they own versus what was handed to them? Candidates who inflated their experience struggled to maintain consistency when Fabric kept digging.
Throughout the interview, Fabric's cheating detection ran in the background. With 48% of technical candidates flagging for cheating behavior across the industry, this mattered. Fabric analyzes signals like response timing, gaze patterns, and language consistency to identify candidates using AI assistance tools. Candidates flagged for cheating were marked in the final shortlist so Sharans could factor that into his evaluation.
What were the results?
Fabric connected to Folens' LinkedIn job post and processed applications as they came in.
2,000+ applications screened. Fabric filtered for relevant technical skills, stack experience, and role fit before any reached Sharans.
Full AI interviews conducted. Candidates who passed resume screening went through the 50-minute technical interview covering coding, system design, and experience.
Top 10 candidates shortlisted. Fabric scored and ranked every candidate, with detailed notes on technical strength, system design maturity, and any cheating flags.
Sharans only needed to speak with a handful of finalists. The entire process, from job post to offer, took three weeks and saved him over 50 hours of screening and interviewing.
For context, hiring a lead engineer for a new team typically takes six to ten weeks when a product leader is running the process without HR support.
How can you set up something similar for your engineering hiring?
If you are hiring engineers without dedicated recruiting bandwidth, here is what the Folens process demonstrates.
Combine multiple evaluation types into one interview. Folens tested coding, system design, and experience in a single 50-minute session. This eliminated the scheduling overhead of multi-round processes.
Make cheating detection part of your process. When nearly half of technical candidates use AI assistance, you cannot rely on interview scores alone.
Define your criteria around the actual role. Folens needed a founding engineer, so the interview emphasized system design and ownership. A mid-level hire at a larger company would need different weighting.
Let the technical leader focus on finals only. Sharans spent his time on candidates who had already been vetted through a rigorous technical screen.
Conclusion
Folens closed a critical founding engineer hire in three weeks, without dedicated HR support, and without Sharans spending weeks buried in resumes and first-round interviews.
The AI interview covered coding, system design, and experience depth in a single session while screening for cheating. Sharans got a ranked shortlist of 10 engineers and only had to personally interview a few finalists.
FAQ
Can an AI interview evaluate system design ability effectively?
Yes. Fabric's AI asks follow-up questions about scalability, tradeoffs, and failure modes based on the candidate's responses. Candidates who give rehearsed or shallow answers get pressed for specifics, which surfaces their actual depth of understanding.
How does Fabric detect cheating during technical interviews?
Fabric analyzes 20+ signals during the interview including response timing, gaze patterns, and language consistency. Candidates using tools like Cluely or ChatGPT voice mode are flagged with a cheating probability score and timestamped evidence.
What is Fabric?
Fabric is an AI interview platform that screens resumes, conducts technical interviews with coding and system design questions, and ranks candidates with built-in cheating detection. It connects to job boards and handles the full top-of-funnel hiring process.
How long does a Fabric engineering interview take?
The Folens interview was 50 minutes and covered coding, system design, and professional experience. Interview length is configurable based on what you need to evaluate.
Can Fabric handle high-volume engineering hiring?
Yes. Fabric processed over 2,000 applications for Folens and conducted full technical interviews for qualified candidates, all without any manual scheduling or screening effort from the hiring team.