Hiring foundational AI talent in 14 days when everyone else failed
Incept Labs builds foundational LLM workflows for education and research—the kind of work that requires engineers who think from first principles, not just follow tutorials. Finding that caliber of talent in India? Nearly impossible. Until they found Utkrusht.
Incept Labs x Utkrusht: Key Takeaways
Sector
AI/ML Research & Development
Requirement
Senior Data Engineer and DevOps Engineer with deep technical expertise, first-principles thinking, and experience scaling infrastructure at high-growth startups
Outcome
Incept Labs hired 2 exceptional engineers in 10-14 days who exceeded performance expectations—after months of failed searches through traditional channels.
What They Were Doing Before
Incept Labs tried everything. And nothing worked.
Job Boards: Posted on LinkedIn, AngelList, Naukri—all the usual suspects. Applications came in, but the quality was abysmal. Junior developers applying for senior roles. People with "Kubernetes" on their resume who'd never actually managed a production cluster. AI engineers who'd fine-tuned one model and called themselves experts.
Recruiters: Worked with multiple recruitment agencies who promised "pre-screened technical talent." The recruiters sent candidates with impressive CVs, but five minutes into a technical conversation, it became clear they didn't understand the fundamentals. They'd memorized frameworks but couldn't think from first principles.
Personal Networks: Reached out to friends, posted in alumni groups, asked for referrals. This yielded a few decent leads, but not nearly enough. When you're building foundational LLM workflows, you need a very specific type of engineer—someone who's seen scale, understands distributed systems, and can architect solutions without hand-holding. Those people aren't sitting in your immediate network waiting for a job.
The process looked like this:
Post the role everywhere and ask everyone they knew
Get flooded with applications from unqualified candidates
Screen 20-30 resumes, most of which were clearly unfit
Interview 5-10 candidates who sounded good on paper
Watch them fail basic first-principles technical questions
Repeat for months
Start questioning if this hire is even possible
They weren't just frustrated. They were stuck. The work couldn't move forward without these engineers, and the engineers they needed simply weren't showing up.
The Challenges & Pain Points
Incept Labs' hiring challenge was different from most startups. They weren't looking for "good" engineers—they were looking for exceptional ones. And those don't grow on trees.
1. High-Skill Requirements That Most Candidates Couldn't Meet
Incept Labs needed engineers who had:
Built and managed Kubernetes clusters at scale: Not "I've used Kubernetes" scale. Real, production, high-traffic, multi-region scale.
First-principles thinking: The ability to look at a problem and architect a solution from scratch, not just copy-paste from Stack Overflow or blindly follow a framework.
Experience at high-growth startups: People who'd seen the chaos of rapid scaling and knew how to build systems that wouldn't break under pressure.
Deep technical fundamentals: Not surface-level knowledge. Real understanding of distributed systems, data pipelines, infrastructure as code, LLM orchestration.
These aren't skills you pick up in a 3-month bootcamp. These are battle scars from years of building real systems at real companies.
And in India, where the talent market skews junior and most engineers learn by following tutorials rather than building from scratch, finding these people was borderline impossible.
2. Lack of Fundamental Skills in the Candidate Pool
The candidates they were seeing fell into two categories:
Category 1: Resume Padders
People who listed every buzzword under the sun—Kubernetes, Docker, Terraform, PyTorch, LangChain, Vector DBs—but couldn't explain how any of it actually worked. They'd followed a tutorial once, added it to their CV, and hoped no one would ask hard questions.
Category 2: Framework Followers
Engineers who could use tools but couldn't think independently. Give them a well-documented framework and they'd be fine. Ask them to solve a novel problem or architect a system from first principles? Blank stares.
Incept Labs didn't need framework followers. They needed builders. People who could look at a complex LLM workflow and figure out how to make it work—even if there wasn't a pre-built solution.
3. Traditional Hiring Methods Couldn't Filter for What Actually Mattered
Here's the problem with resumes and recruiter screens: they optimize for the wrong things.
A resume tells you where someone worked and what technologies they claim to know. It doesn't tell you:
Can they think from first principles?
Have they actually built something complex, or were they just on a team that did?
Can they debug a failing Kubernetes cluster at 2 AM?
Do they understand why things work, or just how to make them work?
Recruiter phone screens are even worse. They ask surface-level questions that anyone can memorize:
"What's the difference between a Pod and a Deployment?"
"Explain how transformers work."
"What's your experience with CI/CD?"
These questions don't reveal depth. They reveal who studied for the interview.
By the time Incept Labs got to the technical interview stage, they'd already wasted hours on candidates who had no business being there. And the right candidates—the ones who could actually do this work—weren't making it through the broken filter.
4. High Agency and Bias for Action Were Non-Negotiable
Incept Labs wasn't just looking for technical skill. They needed people with:
High agency: The ability to take ownership, make decisions, and move forward without constant hand-holding.
Bias for action: The instinct to build and ship, not overthink and wait for perfect information.
These are cultural traits, not technical skills. And they're impossible to assess from a resume or a 30-minute phone screen.
Most candidates they interviewed were technically adequate but operationally timid. They needed someone to tell them what to do. They waited for detailed specifications before writing a line of code. They escalated every decision up the chain.
For a startup building foundational AI infrastructure, that doesn't work. They needed engineers who could see a problem, propose a solution, and execute—without needing their hand held.
How Utkrusht Helped
The breakthrough came through a connection—a mutual undergrad alumni network. But what turned that introduction into a successful hire wasn't the connection. It was the philosophy.
When Incept Labs explained their requirements—senior engineers, first-principles thinkers, Kubernetes at scale, high agency—Utkrusht didn't just nod along. They understood why those requirements mattered and how to actually assess for them.
The Utkrusht Difference:
1. "Build Something" Assessments, Not MCQs or Resume Screening
Most technical assessments are garbage. They ask multiple-choice questions about syntax. They test whether you've memorized framework documentation. They optimize for people who are good at taking tests, not people who are good at building systems.
Utkrusht's assessments are different: candidates have to build something in 20 minutes.
Not answer trivia. Not explain concepts. Not write pseudocode on a whiteboard. Build. Something. That. Works.
For a DevOps engineer: "Here's a broken deployment pipeline. Fix it and optimize for cost."
For a Data engineer: "Here's messy data and a performance bottleneck. Build a pipeline that handles it."
For an AI engineer: "Here's an LLM workflow that's failing. Debug it and make it production-ready."
These aren't questions you can fake your way through. You either know how to build systems, or you don't. The assessment reveals the truth in 20 minutes—no hand-holding, no Googling, no bullshitting.
2. Pre-Vetted Candidates Who'd Already Proven Their Depth
Utkrusht didn't just send random candidates and hope for the best. They had a pre-vetted database of engineers who'd already completed these "build something" assessments.
For Incept Labs, this meant no more resume screening. No more phone screens with candidates who couldn't pass a basic technical test. No more wasted time on people who looked good on paper but couldn't execute.
When Utkrusht sent over 5 candidates, all 5 had already demonstrated:
They could build under pressure
They understood fundamentals, not just frameworks
They could think independently and solve novel problems
They had the depth of experience Incept Labs needed
The filtering had already happened. Incept Labs just needed to pick the best fit for their team.
3. High-Skill, High-Agency Talent in India
Finding senior engineers in India who meet Silicon Valley standards is hard. Finding ones with high agency and bias for action is even harder.
Utkrusht specialized in exactly this: high-skill, high-agency technical talent that most companies in India can't access.
They weren't sending fresh graduates or mid-level engineers pretending to be senior. They were sending people who'd worked at high-growth startups, built systems at scale, and had the battle scars to prove it.
And critically, they were sending people who didn't need their hands held. Engineers who could take a vague problem, architect a solution, and execute without waiting for perfect instructions.
That's rare. And that's what Incept Labs needed.
The Process:
Introduction through alumni network: A mutual connection made the intro, but the real work started after that.
Incept Labs explained their requirements: Senior Data Engineer and DevOps Engineer with Kubernetes at scale, first-principles thinking, and high agency.
Utkrusht sourced candidates from their pre-vetted database: Including one engineer Utkrusht's founder had personally worked with before—someone who'd already proven their caliber.
Candidates completed "build something" assessments: 20-minute real-world technical challenges that revealed depth, not just surface knowledge.
Utkrusht sent 5 qualified candidates: All had passed the assessments. All had the technical depth Incept Labs needed.
Incept Labs interviewed and hired 2: One Data Engineer, one DevOps Engineer. Both cleared technical rounds easily because the assessments had already validated their skills.
Timeline: 10-14 days from first contact to offers accepted.
"We spent months looking for engineers who could think from first principles and handle the scale we needed. Job boards gave us junior candidates. Recruiters gave us resume padders. Our networks dried up. We were starting to think this hire was impossible—until Utkrusht showed us it wasn't."
— Incept Labs Founding Team
The Results
Incept Labs didn't just fill two roles. They found two engineers who exceeded expectations—something that almost never happens when you're hiring for highly specialized, senior positions.
Time to Hire: 10-14 Days (vs. Months of Failed Searching)
Before Utkrusht:
Months of posting on job boards, working with recruiters, and tapping networks
Zero qualified candidates found
Growing frustration and project delays
With Utkrusht:
10-14 days from first conversation to offers accepted
5 pre-vetted candidates delivered
2 exceptional hires who hit the ground running
That's not just faster—it's the difference between hiring being a blocker and hiring being solved.
Candidate Quality: 5 Strong Candidates, 2 Exceptional Hires
Before Utkrusht, the funnel looked like this:
100+ applications → 20-30 resume screens → 5-10 interviews → 0 qualified candidates
With Utkrusht:
5 candidates delivered → 5 interviews → 2 hires who exceeded expectations
That's a 40% conversion rate from candidates delivered to exceptional hires. In senior technical hiring, that's almost unheard of.
Performance: Exceeded Expectations
This is the real test of any hire: How are they doing six months later?
Most hires fall into one of three buckets:
Regret hires: They're not working out. Either they'll be fired or they'll quit.
Adequate hires: They're fine. Not great, not terrible. They do the job.
Exceptional hires: They're better than you hoped. They're shipping faster, thinking deeper, and elevating the team.
Incept Labs' two hires from Utkrusht? Category 3. They exceeded expectations.
They didn't just meet the technical bar—they raised it. They didn't just follow instructions—they took ownership and drove outcomes. They didn't just do the work—they made everyone around them better.
That's what happens when you hire for depth, not just credentials.
Cost Savings: Impossible to Quantify, Impossible to Ignore
Let's do the math on what Incept Labs avoided:
Months of failed hiring:
3+ months of an empty role = 3 months of project delays, missed milestones, and competitive disadvantage
Founder/CTO time wasted on bad interviews = 40+ hours @ $200+/hour = $8,000+ in opportunity cost
Recruiter fees for failed placements = $10,000+ wasted
Bad hires they avoided:
1 bad senior hire = 6-12 months of reduced productivity + severance + team morale damage = $100,000+ in total cost
Utkrusht didn't just save time. They saved Incept Labs from the compounding cost of hiring wrong or not hiring at all.
Philosophy Shift: Assessment-First Hiring for Senior Roles
Incept Labs learned something that will change how they hire forever:
For senior, specialized roles, resume-first hiring is a waste of time. Assessment-first hiring is the only approach that works.
Resumes lie. Interviews are theater. Credentials are meaningless if someone can't build.
But when you make someone build something in 20 minutes—no Google, no hand-holding, just raw technical skill—you learn everything you need to know.
Can they think from first principles? The assessment shows you.
Do they understand fundamentals or just frameworks? The assessment shows you.
Can they execute under pressure? The assessment shows you.
That's the future of senior technical hiring. And Incept Labs is never going back to the old way.
What Stood Out Most
When we asked Incept Labs what made the biggest difference, they pointed to one thing: how Utkrusht assesses candidates.
"Build Something" > Resume Knowledge
Most technical assessments are broken. They test whether you can answer questions about concepts—not whether you can apply those concepts to solve real problems.
Examples of broken assessments:
MCQ questions: "What does the kubectl get pods command do?" (Who cares? Google exists.)
Whiteboard coding: "Reverse a binary tree." (When was the last time you reversed a binary tree at work?)
Framework trivia: "Explain the difference between useEffect and useLayoutEffect." (This tests memorization, not skill.)
These assessments optimize for people who are good at taking tests. They don't optimize for people who are good at building systems.
Utkrusht's assessments are different: "Here's a real problem. Build something that solves it. You have 20 minutes."
For DevOps engineers:
"This deployment pipeline is broken and costs too much. Fix it."
"Optimize this Kubernetes cluster for scale and cost efficiency."
"Debug this infrastructure failure and prevent it from happening again."
For Data engineers:
"This data pipeline is slow and buggy. Refactor it."
"Process this messy dataset and build a clean pipeline."
"Design a system that handles 10x the current load."
For AI engineers:
"This LLM workflow is failing. Debug and fix it."
"Optimize this inference pipeline for latency and cost."
"Build a system that handles concurrent requests at scale."
These aren't trick questions. They're real problems that real engineers face at real companies. And they reveal everything:
✅ Can you think from first principles?
✅ Do you understand fundamentals or just frameworks?
✅ Can you execute under pressure?
✅ Can you debug, optimize, and architect—not just code?
For Incept Labs, this was the breakthrough. They'd been trying to assess these qualities through resumes and interviews for months. It didn't work.
Utkrusht's "build something" assessments revealed depth in 20 minutes that a resume never could.
Why This Matters for Foundational AI Work
When you're building foundational LLM systems, you can't afford to hire people who just follow tutorials. You need people who can:
Architect novel solutions to problems that don't have Stack Overflow answers
Debug complex distributed systems without hand-holding
Optimize for scale, cost, and latency simultaneously
Think 10 steps ahead and build systems that won't break under pressure
These are the engineers who've been in the trenches. Who've built production systems at scale. Who've made mistakes, learned from them, and come out better.
And you can't find them through job boards or recruiters. You find them by making them build something—and seeing if they can actually do it.
That's what Utkrusht did. And that's why it worked.
Why Incept Labs Chose Utkrusht Over Others
Incept Labs had tried everything before Utkrusht. Job boards, recruiters, personal networks—all failed. So why did Utkrusht succeed when everyone else couldn't?
1. Specialized in High-Skill Technical Talent in India
Most platforms and recruiters operate at scale. They're designed for companies hiring 100+ generic software engineers, not startups hiring 2 exceptional senior engineers for foundational AI work.
Utkrusht is different: they specialize in high-skill, hard-to-find technical talent that most companies in India can't access.
They're not sending fresh graduates. They're not sending mid-level engineers with inflated titles. They're sending people who've worked at high-growth startups, built systems at scale, and have the depth that senior roles actually require.
For Incept Labs, this was critical. They weren't looking for "good enough." They needed exceptional. And Utkrusht delivered.
2. Assessment Philosophy That Actually Tests What Matters
Incept Labs didn't need another recruiter asking surface-level screening questions. They needed someone who understood:
What first-principles thinking looks like in practice
How to assess for Kubernetes expertise at scale (not just "I've used Kubernetes")
What high agency and bias for action actually mean in engineering work
Utkrusht's "build something" assessments tested for all of this. And when Incept Labs saw the assessment results, they immediately understood why these candidates were different from everyone else they'd interviewed.
The assessments revealed: ✅ Depth of understanding: Not just "I know Docker," but "I can architect containerized systems for scale and cost efficiency."
✅ First-principles thinking: The ability to solve novel problems without relying on pre-built solutions.
✅ Execution speed: Can they build under pressure, or do they freeze?
✅ Technical judgment: Do they make smart trade-offs between speed, cost, and reliability?
This is what resumes and phone screens can never reveal. But a 20-minute "build something" assessment? It shows you everything.
3. Pre-Vetted Database Saved Months of Wasted Time
Incept Labs didn't have time to interview 50 candidates hoping to find 1 good one. They needed a shortlist of people who'd already proven their technical depth—and that's exactly what Utkrusht provided.
By maintaining a pre-vetted database of engineers who'd completed rigorous assessments, Utkrusht eliminated the most painful part of hiring: the endless screening of unqualified candidates.
When Utkrusht said "These 5 candidates are good," they weren't guessing. They had data. The candidates had already built something real and demonstrated their skills objectively.
For Incept Labs, this meant:
No wasted time on unqualified candidates
No "great resume, can't code" surprises
No second-guessing whether someone could actually do the work
The technical validation had already happened. They just needed to pick the best cultural fit.
4. They Actually Understood the Problem
Most recruiters treat all "tech roles" the same. They don't understand why hiring a DevOps engineer for foundational AI infrastructure is different from hiring a DevOps engineer for a SaaS app. They don't get why Kubernetes "at scale" means something completely different from "I deployed a cluster once."
Utkrusht understood. When Incept Labs explained:
"We need first-principles thinkers"
"We need people who've seen Kubernetes at real scale"
"We need high agency and bias for action"
Utkrusht didn't just nod along. They knew exactly what those requirements meant and how to assess for them.
That level of understanding is rare. And it made all the difference.
"Most assessments test whether you've memorized things. Utkrusht's assessments test whether you can actually build. That's the difference between hiring someone who talks well and hiring someone who ships. We got the latter—twice."
— Incept Labs Founding Team
What's Next
Incept Labs isn't going back to job boards and recruiters. They've seen what assessment-first hiring can do, and the difference is night and day.
When they need to scale their team further—whether it's more AI engineers, infrastructure engineers, or data engineers—they know exactly where to go.
They're not posting on LinkedIn and hoping. They're not working with recruiters who send generic candidates. They're not wasting months on people who can't pass basic technical challenges.
They're going straight to Utkrusht's pre-vetted database, getting a shortlist of candidates who've already proven they can build, and making hiring decisions based on objective data—not gut feelings and wishful thinking.
For companies building foundational AI systems where technical depth is everything, assessment-first hiring isn't optional—it's the only approach that works.
"We needed engineers who could build foundational AI systems, not just use pre-built tools. Utkrusht's 'build something' assessments filtered for exactly that—and both hires exceeded our expectations. That doesn't happen by accident. It happens when you assess for what actually matters."
— Incept Labs Founding Team
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