Hiring a perfect fit DevOps Engineer in 9 days
Unravel Tech provides engineering solutions to companies building complex systems. Finding a senior DevOps engineer who could actually execute—not just talk—had been a months-long nightmare. Until they stopped screening resumes and started looking at real data.
Unravel Tech x Utkrusht: Key Takeaways
Sector
Engineering Solutions Provider
Requirement
Senior DevOps Engineer with deep technical fundamentals, high agency, and proven ability to build and ship
Outcome
Unravel Tech hired Aviral in 9 days—a perfect cultural and technical fit who ticked every single requirement box—after months of failed attempts through agencies and communities.
What They Were Doing Before
Unravel Tech was doing what most companies do when they need to hire: the manual grind.
Recruitment Agencies: Paying fees to agencies who'd blast job descriptions everywhere and send back a flood of candidates. The problem? The agencies didn't understand what "senior DevOps engineer" actually meant. They sent people with "Kubernetes" on their resume who'd never managed a production cluster. They sent candidates who could talk the talk but couldn't walk the walk.
Community Sourcing: Posting in Slack groups, LinkedIn communities, Discord channels. Hoping someone would see the post, know someone good, and make a referral. This worked occasionally, but the quality was inconsistent and the volume was low. Most referrals were friends helping friends—not necessarily the best technical fits.
Manual Screening & Assessment: Once candidates came in (from agencies or communities), someone on the team had to:
Read through resumes and try to figure out who was legit
Do phone screens to filter out the obviously unqualified
Schedule technical interviews for the ones who seemed promising
Watch most of them fail basic technical questions
Start over
The process was slow, manual, and exhausting. Every step required human judgment with incomplete information. Every candidate was a gamble.
Worst of all? The people making it through the filter often weren't the best candidates—they were just the best at gaming the process.
Good interviewers who couldn't ship. Strong resumes who couldn't execute. Candidates who knew the buzzwords but not the fundamentals.
Unravel Tech was stuck in a loop: interview, reject, repeat. For months.
The Challenges & Pain Points
Unravel Tech's hiring problem wasn't unique—but it was painful.
1. Manual Sourcing Is a Black Hole
When you're manually sourcing candidates, you're flying blind. You post on job boards and communities, then hope good people see it and apply. But most of the time:
The best candidates aren't actively looking (they're busy working)
The people who do apply are mostly unqualified
The signal-to-noise ratio is abysmal (100 applications, 2 worth interviewing)
Recruitment agencies are supposed to solve this, but they don't. They optimize for volume, not quality. They send everyone who vaguely matches the job description and let you figure out who's actually good.
The result? Unravel Tech was drowning in mediocre candidates while the right people never made it to their inbox.
2. Manual Assessment Wastes Everyone's Time
Once candidates made it through the initial screen (resume → phone call), Unravel Tech had to assess them manually:
Schedule technical interviews
Spend 1-2 hours per candidate evaluating their skills
Realize most couldn't pass basic technical challenges
Repeat
This meant their senior engineers—the people who should be building product—were spending 10+ hours per week interviewing candidates who had no business being there.
And here's the kicker: manual assessment is subjective. Different interviewers have different standards. Some candidates are great at interviewing but terrible at executing. Others are quiet in interviews but ship like machines.
You can't tell who's who until after you hire them—and by then it's too late.
3. No Data, Just Gut Feelings
When you're hiring manually, every decision is a guess:
"Their resume looks good, let's interview them."
"They seemed smart in the phone screen, let's bring them in."
"I have a good feeling about this one."
But gut feelings aren't reliable. And resumes lie. And interviews are theater.
What Unravel Tech needed was objective data showing:
Can this person actually build things, or just talk about building things?
Do they have deep fundamentals, or surface-level framework knowledge?
Are they self-starters with high agency, or do they need hand-holding?
Will they actually accept an offer if we make one?
Without that data, every hire was a gamble. And Unravel Tech was tired of losing.
4. The Best Candidates Weren't Even Making It Through
Here's the worst part about manual sourcing and assessment: you're probably missing the best people.
The best engineers often:
Don't have polished resumes (they're too busy building)
Aren't active on job boards (they get opportunities through networks)
Don't interview well (they're introverts who'd rather code than talk)
Get filtered out early because they don't "look" impressive on paper
Meanwhile, the people who do make it through are often:
Great at selling themselves (but can't deliver)
Good at interviews (but can't execute under pressure)
Strong on credentials (but weak on fundamentals)
Unravel Tech realized they were optimizing for the wrong things. They weren't finding the best engineers—they were finding the best interviewees.
How Utkrusht Helped
Unravel Tech's breakthrough came when they stopped trying to assess candidates manually and started looking at objective data first.
Here's what changed: instead of screening resumes and hoping, they got pre-assessed candidates with detailed analytics showing exactly what each person could do.
The Utkrusht Difference:
1. Active Sourcing, Not Passive Posting
Most companies post jobs and wait. Utkrusht flipped the script: they went out and found the candidates Unravel Tech needed.
The process:
Utkrusht identified 10+ strong DevOps candidates who matched the technical profile
Called each one to do a quick qualification check (not a surface-level screen—actual questions about their work)
Invited qualified candidates to complete technical assessments
Ended up running 120 assessments to build a strong pipeline
That's not spray-and-pray sourcing. That's targeted, high-quality candidate identification.
By the time candidates reached Unravel Tech, they'd already been filtered twice:
Utkrusht's initial qualification call (can they talk intelligently about their work?)
The technical assessment (can they actually build something?)
No more wading through 100 unqualified applications. No more wasting time on people who couldn't code. Just strong candidates who'd already proven their skills.
2. "Build Something" Assessments That Reveal Real Skill
Utkrusht's assessments don't test trivia or framework knowledge. They test whether you can build and ship.
For a DevOps engineer:
"Here's a broken CI/CD pipeline. Fix it and optimize for cost."
"This Kubernetes cluster is failing under load. Debug and scale it."
"Design an infrastructure setup that handles 10x traffic without breaking the bank."
These aren't questions you can fake. You either know how to architect systems, or you don't. The assessment reveals the truth in 20 minutes.
Out of 120 candidates who took assessments, Utkrusht identified 8 who demonstrated: ✅ Strong grasp of fundamentals (not just framework knowledge)
✅ Ability to build and ship under pressure
✅ First-principles thinking (not just copying from Stack Overflow)
✅ High technical depth across infrastructure, CI/CD, and orchestration
Those 8 candidates weren't just "good"—they were the best out of 120. Unravel Tech didn't have to guess. The data showed it.
3. Detailed Candidate Research That Changed Decision-Making
Here's where Utkrusht really stood out: the way they presented candidates.
Most recruiters send a resume and maybe a short blurb. "Here's John, he has 5 years of DevOps experience, let me know if you want to interview him."
That's useless. It doesn't tell you:
Can John actually build things?
Is John a self-starter or does he need hand-holding?
Will John accept your offer if you make one?
What makes John different from the other 50 DevOps candidates?
Utkrusht sent detailed PDFs for each candidate that included:
📊 Assessment Analytics:
Link to the full technical assessment
Skill score (objective measure of technical depth)
Performance breakdown (where they excelled, where they struggled)
🎯 Signal Analysis:
High agency indicators: Self-starters who don't need spoon-feeding
Intent to join: How motivated they are to switch (not just casually browsing)
Salary expectations: Does it fit your budget, or will you waste time negotiating?
Location preferences: Are they open to your setup (remote/hybrid/office)?
🔍 Unique Context:
What makes this person special beyond their resume
Examples of past work that show execution ability
Cultural fit indicators (for Aviral: "Took a college drop to pursue guitar professionally—shows agency and conviction in following through on commitments")
This wasn't fluff. These were actionable signals that helped Unravel Tech make faster, smarter decisions.
When they looked at Aviral's profile, they didn't just see "DevOps engineer with Kubernetes experience." They saw:
Top-tier technical assessment score
High agency (proven through past decisions like the guitar story)
Strong intent to join (actively looking, realistic salary expectations)
Perfect cultural fit (self-reliant, bias for action, doesn't need micromanagement)
That's the kind of information that turns hiring from guesswork into data-driven decision-making.
4. Assessment Dashboard with Real-Time Analytics
Beyond the candidate PDFs, Utkrusht provided a dashboard showing:
Who took assessments (and who didn't—indicating low intent)
Who got shortlisted based on objective criteria
Skill score distributions across the candidate pool
Intent-to-join signals for each candidate
This gave Unravel Tech visibility into the entire funnel:
120 assessments administered
8 strong candidates identified (top 6-7%)
2 shortlisted for final interviews
1 hired (Aviral)
No more black box. No more "we sent you 10 candidates, good luck." Just clear data showing exactly where each candidate stood and why.
The Process:
Unravel Tech explained their requirements: Senior DevOps engineer with strong fundamentals, high agency, and ability to ship.
Utkrusht sourced 10+ candidates through targeted outreach, then qualified them with real technical questions about their work.
120 candidates completed assessments over the course of the engagement (building a strong pipeline for current and future needs).
Utkrusht identified 8 exceptional candidates who scored well on technical depth + had the right cultural signals (high agency, intent to join, salary fit).
Utkrusht sent detailed candidate reports with assessment links, skill scores, intent analysis, and unique context for each person.
Unravel Tech interviewed 8 candidates and shortlisted 2 for final rounds—both were strong, but Aviral was the perfect fit.
One candidate rejected Unravel Tech's offer (they got a competing offer), but Aviral accepted.
Timeline: 9 days from introduction to hire.
"We were spending weeks manually screening candidates from agencies and communities, then watching them fail our technical interviews. It wasn't a pipeline problem—we had candidates. It was a quality problem—none of them could actually do the work. We needed a way to filter for skill before wasting everyone's time."
— Unravel Tech Engineering Team
The Results
Unravel Tech didn't just fill a role. They found someone who ticked every single box—technical depth, cultural fit, high agency, realistic salary—and hired them in 9 days.
Time to Hire: 9 Days (vs. Months of Manual Sourcing)
Before Utkrusht:
Months of posting on job boards, working with agencies, and sourcing through communities
Endless manual screening and phone calls
Multiple failed interview cycles with candidates who couldn't pass technical rounds
With Utkrusht:
9 days from introduction to offer accepted
8 pre-assessed candidates delivered (all technically qualified)
2 shortlisted for final rounds
1 hired (Aviral)
That's not just faster—it's the difference between hiring being a months-long blocker and hiring being solved in a week and a half.
Candidate Quality: 120 Assessments → 8 Strong Candidates → 1 Perfect Hire
The funnel tells the story:
Before Utkrusht:
100+ applications from agencies/communities → 10-15 phone screens → 3-5 technical interviews → 0 qualified hires
With Utkrusht:
120 assessments administered → 8 exceptional candidates (top 6-7%) → 2 finalists → 1 hire who exceeded expectations
That's a 12.5% conversion rate from strong candidates to hire. In senior DevOps hiring, that's exceptional.
But here's what matters more: the hire quality. Aviral didn't just meet the bar—he was the perfect fit.
Hire Quality: Ticked Every Single Requirement Box
When you're hiring manually, you usually end up compromising:
"They're strong technically, but they might need some management."
"They're a great culture fit, but their Kubernetes experience is light."
"They're expensive, but they're the best we've seen in months."
With Aviral, Unravel Tech didn't have to compromise on anything:
✅ Technical depth: Strong grasp of fundamentals, not just framework knowledge
✅ Execution ability: Could build and ship, not just talk about building
✅ High agency: Self-starter who doesn't need hand-holding
✅ Cultural fit: Perfect match for Unravel Tech's bias-for-action culture
✅ Salary expectations: Within budget—no awkward negotiations
✅ Intent to join: Actually wanted the role (not just testing the market)
That's what happens when you have data before you interview, not after.
Team Productivity: 10+ Hours Per Week Saved
Before Utkrusht, Unravel Tech's senior engineers were spending half their week interviewing candidates who couldn't pass basic technical challenges.
With Utkrusht's pre-assessment model:
Only interviewed candidates who'd already proven their technical depth
Cut interview time by 70-80% (8 qualified candidates vs. 30+ unqualified ones)
Senior engineers could focus on building product, not screening resumes
That's 40+ hours per month back in the hands of people who should be shipping, not interviewing.
Decision-Making: From Gut Feelings to Data-Driven Hiring
The biggest shift wasn't speed—it was confidence.
Before Utkrusht, every hire decision was a guess:
"Their resume looks good, let's hope they can execute."
"They interviewed well, let's hope they're not just good at talking."
"I have a good feeling about this one, let's hope it works out."
With Utkrusht's detailed candidate reports, Unravel Tech had objective data showing:
Skill scores: Not opinions—actual performance on technical assessments
Intent to join: Not guessing—real signals about motivation and fit
Cultural indicators: Not assumptions—evidence of high agency and bias for action
Salary alignment: Not surprises—upfront clarity on expectations
When they made Aviral an offer, they weren't hoping. They knew he was the right hire. The data proved it.
Cost Savings: Months of Wasted Time Avoided
Let's do the math on what Unravel Tech avoided:
Failed sourcing attempts:
3+ months of manual sourcing through agencies/communities = 3 months of an empty role
Agency fees for failed placements = $10,000+ wasted
Senior engineer time on bad interviews = 50+ hours @ $150+/hour = $7,500+ in opportunity cost
Bad hires they avoided:
1 bad senior hire = 6-12 months of reduced productivity + severance + restarting the search = $80,000+ in total cost
Utkrusht didn't just save time. They saved Unravel Tech from the compounding cost of hiring wrong or not hiring at all.
What Stood Out Most
When we asked Unravel Tech what made the biggest difference, they pointed to two things: the quality of candidate research and the actionable signals that made decision-making easy.
Detailed Candidate Research > Generic Resume Forwarding
Most recruiters treat candidate presentation like an afterthought. They forward a resume, maybe a LinkedIn profile, and say "Let me know if you want to talk."
That approach is lazy and unhelpful. It doesn't answer the questions hiring managers actually care about:
Can they execute? (Or just interview well?)
Are they self-starters? (Or do they need micromanagement?)
Will they accept an offer? (Or are they just fishing for counteroffers?)
What makes them special? (Beyond bullet points on a resume?)
Utkrusht's candidate reports answered all of these—before the first interview.
For Aviral, the report included:
Assessment performance: Scored in the top tier on DevOps fundamentals, infrastructure design, and CI/CD implementation
Intent to join: Actively looking for the right opportunity (not passively browsing)
Salary expectations: Aligned with Unravel Tech's budget
High agency signal: Story about taking a college drop to pursue guitar professionally—showed he follows through on commitments and doesn't need external validation to make bold decisions
Cultural fit indicators: Self-reliant, bias for action, doesn't wait for perfect information
This level of research completely changed how Unravel Tech evaluated candidates. They weren't reading resumes and guessing anymore. They had objective data and contextual signals showing who was worth their time.
Actionable Signals That Make Hiring Decisions Easy
The second thing that stood out was the rubric-based assessment insights Utkrusht provided through their analytics dashboard.
For each candidate, Unravel Tech could see:
📊 Skill Score
Not a subjective "seems good"—an objective measure of technical performance on real-world challenges. Aviral scored in the top 10% of all assessed DevOps candidates.
🎯 Intent to Join
Was this person actively looking, or just testing the market? Aviral had high intent—he was ready to move for the right opportunity, which meant Unravel Tech wasn't wasting time on someone who'd reject their offer.
💰 Salary Range
No surprises in negotiation. Utkrusht provided upfront clarity on expectations, so Unravel Tech knew Aviral was within budget before investing interview time.
🧠 Fundamentals vs. Framework Knowledge
Could this person think from first principles, or did they just know how to use tools? The assessment showed Aviral had deep fundamentals—he could architect systems, not just deploy pre-built ones.
🚀 Execution Ability
Could they build and ship under pressure, or freeze when things got hard? Aviral's assessment performance showed he could execute—not just talk about executing.
These signals weren't fluff. They were decision-making tools that helped Unravel Tech move fast without sacrificing quality.
When they looked at the candidate dashboard and saw:
Aviral: Top-tier skill score + high intent + salary fit + strong fundamentals + high agency
...the decision was obvious. They didn't need to interview 20 people to find one good one. They interviewed 8 pre-qualified candidates and hired the best fit in 9 days.
Why This Matters for Senior DevOps Hiring
DevOps engineers are hard to assess from resumes. Everyone lists the same keywords: Kubernetes, Docker, Terraform, CI/CD, AWS, monitoring, scaling.
But those keywords don't tell you:
Have they managed infrastructure at scale, or just deployed a hobby project?
Can they debug production incidents at 2 AM, or do they panic under pressure?
Do they understand cost optimization, or just throw money at servers?
Can they architect systems from scratch, or only work with pre-built templates?
These are the questions that actually matter. And you can't answer them from a resume or a phone screen.
Utkrusht's assessments + detailed candidate research answered them upfront. That's why Unravel Tech could hire fast and hire right.
Why Unravel Tech Chose Utkrusht Over Others
Unravel Tech had been working with recruitment agencies and sourcing through communities for months. So why did Utkrusht succeed when everyone else failed?
1. Active Sourcing, Not Passive Waiting
Agencies post your job and wait for applications. Communities rely on someone seeing your post and referring someone. Both approaches are passive—you're hoping good candidates find you.
Utkrusht was active: they went out and identified 10+ strong DevOps candidates, called them, qualified them, and ran assessments to validate their skills.
That's not hoping. That's hunting.
2. Assessment-First Filtering
Most recruiters screen for "looks good on paper." They forward everyone who has the right keywords on their resume and let you figure out who's actually qualified.
Utkrusht screened for "can actually build." They ran 120 assessments and only sent the top 6-7%—the candidates who'd proven their technical depth objectively.
By the time candidates reached Unravel Tech, the hard filtering was done. No more wasting time on people who couldn't code.
3. Detailed Candidate Intelligence
Agencies send resumes. Utkrusht sent intelligence reports with:
Assessment scores and performance breakdowns
Intent-to-join signals
Salary expectations
High agency indicators
Cultural fit context
This wasn't just "here are some candidates"—it was "here's exactly what you need to know to make a smart hiring decision."
4. Philosophy Alignment
When Unravel Tech talked to Utkrusht about their assessment philosophy—"build something real in 20 minutes, not MCQs or trivia"—it clicked immediately.
They'd been burned too many times by candidates who interviewed well but couldn't execute. They were tired of resume theater and subjective phone screens.
Utkrusht's approach—prove you can build first, then we'll talk—resonated. It's how hiring should work for technical roles.
5. Analytics Dashboard for Decision-Making
Beyond individual candidate reports, Utkrusht provided a dashboard showing the entire pipeline:
Who took assessments
Who got shortlisted (and why)
Skill score distributions
Intent signals for each candidate
This gave Unravel Tech visibility they'd never had before. They could see exactly where each candidate stood, compare performance objectively, and make data-driven decisions instead of relying on gut feelings.
That level of transparency and data is rare in recruiting. And it made all the difference.
"The difference was in the research. Most recruiters send a resume and say 'let me know if you want to talk to them.' Utkrusht sent detailed reports showing skill scores, intent to join, salary expectations, and context about what made each person special. We weren't guessing anymore—we had data. That's why we could move fast and hire with confidence."
— Unravel Tech Engineering Team
What's Next
Unravel Tech isn't going back to manual sourcing and assessment. They've seen what data-driven hiring looks like, and there's no unseeing it.
When they need to hire their next senior engineer—whether DevOps, backend, data, or AI—they know the playbook:
Don't post and pray on job boards
Don't hope agencies send good candidates
Don't waste time manually screening resumes
Instead:
Let Utkrusht actively source strong candidates
Use "build something" assessments to validate technical depth
Review detailed candidate intelligence with skill scores, intent signals, and cultural fit context
Interview only the top-tier candidates who've already proven they can execute
Make offers fast with confidence backed by data
For companies where senior technical roles are mission-critical, assessment-first hiring with detailed candidate research isn't optional—it's the only approach that works.
"Aviral ticked every single box we had. Strong technical fundamentals. High agency. Perfect culture fit. Reasonable salary expectations. We didn't have to compromise on anything. That doesn't happen by accident—it happens when you have real data on candidates before you interview them, not after."
— Unravel Tech Engineering Team
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