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MAS Hire · Powered by Mr Hire AI
AI hiring agents are everywhere.
Most send bad candidates, faster.
Mr Hire is different. He was trained on the 20,000+ engineers MAS developed itself — through Mr Learn, Mr Mentor, and Mr Test. When he scores a candidate as deploy-ready, he's matching against people who actually got placed and stayed.
"Don't screen talent. Develop it. Then deploy it."
— The MAS thesis, since 2019
mr-hire.agent
Top Matches
A
Arjun P.
Data Engineer · 6 yrs
96%
P
Priya K.
Data Engineer · 5 yrs
88%
R
Rahul M.
Data Engineer · 4 yrs
72%
Screening 1,247 profiles...
✨
Ready to deploy
Found in 4 seconds
1,500+
Engineers placed via Mr Hire
250+
Companies hired through MAS
15.2 LPA
Average CTC, FY24
79.5%
Placed above 12 LPA
44 LPA
Highest package, FY24
<7 days
Median time-to-shortlist
1,500+
Engineers placed via Mr Hire
250+
Companies hired through MAS
15.2 LPA
Average CTC, FY24
79.5%
Placed above 12 LPA
44 LPA
Highest package, FY24
<7 days
Median time-to-shortlist
State of hiring
The
state
of hiring,
2026.
Every hiring stack now claims to use AI. Voice agents, LLM screeners, autonomous shortlisters. The result: your inbox has more shortlists than ever — and your engineering manager still says "none of these are hireable."
The problem isn't the AI. It's the data. Most platforms scrape resumes off the open web and call it a model. We didn't.
For five years, MAS has trained, mentored, tested, and placed engineers. Every placement, every interview signal, every dropped candidate, every promoted hire — it all becomes the ground truth Mr Hire learns from.
A short note before the rest of this page
01 / Meet the agent
A hiring agent with a clear job description.
In 2026, the trust killer is opaque AI. Here's exactly what Mr Hire does, what he flags, and what he never touches.
M
Mr Hire
Autonomous Recruiting Agent
Built on the proprietary MAS talent graph. Runs voice interviews, scores candidates, ranks shortlists, schedules loops. Reports to your TA team.
Model class
LLM + ranking
Trained on
20K+ placements
Languages
EN, HI, regional
Audit cadence
Quarterly
Capability boundary
No hand-waving. This is what Mr Hire is allowed to do, what he escalates, and what he is forbidden from touching.
DOES
Parses JDs, builds skill profiles, screens 1,000+ resumes in parallel
Scores candidates on technical depth, role fit, and compensation alignment
DOES
Conducts voice interviews 24/7, evaluates real-world coding scenarios
Generates ranked shortlists with explainable scoring and interview transcripts
FLAGS
Edge cases: cultural-fit ambiguity, salary outliers, resume gaps over 6 months
Always routed to a human MAS recruiter before reaching your team
NEVER
Makes the final hire decision. Negotiates salary autonomously.
Final authority always remains with your hiring manager, full stop.
02 / The moat
Other agents screen talent. Mr Hire was trained on talent we developed.
Five years. 250+ companies. 800+ placed engineers. This is the ground truth nobody else has.
01
TRAIN
Mr. Learn
20,000+ learners trained across Data Science, AI, Cloud, ERP, Product, Consulting. Industry-aligned curriculum, refreshed quarterly.
20K+ engineers
02
COACH
Mr. Mentor
1,000+ IIT alumni mentors on a token-based model. Live 1-on-1 interview prep, mock loops, role-specific coaching.
1,000+ mentors
03
VALIDATE
Mr. Test
Real-world scenario testing, not LeetCode. Production code debugging, ML model design, system architecture, business cases.
1,500+ B2B test deliveries
04
DEPLOY
Mr. Hire
Autonomous recruiting agent. Sees every signal from layers 01–03, scores candidates against actual placement outcomes, deploys.
800+ placements
“Most 'AI hiring' platforms screen the open market and hope for the best. Mr Hire ranks candidates against a private ground truth — the MAS placement graph. Five years. 250+ companies. 800+ placed engineers. Tracked through onboarding, ramp, and retention.”
Why this is hard to replicate
03 / Live demo
Drop in a JD. Watch Mr Hire score in real time.
Sandboxed simulation using anonymized profiles from the MAS bench. No signup needed.
mr-hire.in / sandbox / v2.4
LIVE
Job Description
Sandbox uses synthetic candidate profiles. Production agent screens up to 1,000 candidates per JD with full audit trail.
Awaiting JD input...
04 / How companies engage
Three engagement models. Pick what your hiring problem actually looks like.
No one-size-fits-all. Built to match the scale and urgency of your actual problem.
SPOT HIRES
One specialist at a time.
Pay-per-hire · 8.33–12% of first-year CTC
- →
Single specialist roles in AI/ML, Data Eng, Cloud, ERP
- →
Median time-to-shortlist: under 7 days
- →
Your first hire is on us — full service, no cost
- →
Replacement guarantee within 90 days
Best for · Series A → C, GCCs
Start →DEPLOY-ON-DEMAND BENCH
Pre-vetted engineers, ready to start Monday.
Contract / Contract-to-hire · monthly billing
- →
200+ pre-vetted engineers on the MAS bench (2–18 yrs)
- →
ServiceNow · Salesforce · SAP · AWS · Azure · GCP certified
- →
Onsite, offshore, or hybrid delivery models
- →
Convert to FTE anytime · pre-negotiated terms
Best for · GCCs, consulting firms, BFSI
Start →BULK COHORTS
Hire 25, 50, or 100+ engineers in a quarter.
Fixed-price cohort · custom SLAs
- →
Premium bulk hiring, sourced from MAS placement pipeline
- →
Cohort-level guarantees on quality, ramp time, retention
- →
Custom upskilling for your stack (delivered via Mr Learn)
- →
Shared dashboard for your TA + engineering leadership
Best for · GCC build-outs, SI partners
Start →05 / Case study
2024–25
Banking & Financial Services
Standard Chartered, GBS Bengaluru.
Data Analytics build-out.
“When we needed to scale our analytics function across three product lines, MAS sent us engineers who shipped working dashboards in week one — not week six. The pipeline didn't just hit its numbers; it changed how we think about our hiring funnel.”
— Engineering Director, SCB GBS · Reference available under NDA
Outcomes at a glance
Roles closed
42
Time to first hire
9 days
Cohort completion
11 wks
Retention @ 6 months
94%
* Aggregated outcomes across two cohort batches. Full case study and reference call available on request.
06 / The roster
250+ companies have hired through MAS. Here's a slice.
BFSI, Global Capability Centres, Consulting, Consumer-tech — across the spectrum.
Morgan Stanley
Standard Chartered
JPMorgan
HSBC
Barclays
Citi
NatWest
Mastercard
ICICI Bank
Amex
Bharat Petroleum
Futures First
Microsoft
Oracle
HCLTech
LTIMindtree
Infosys
Capgemini
Accenture
IBM
Wavericks
Prodapt
Sprinklr
Rippling
600+
Students placed
250+
Companies hired
15.2 LPA
Average CTC, FY24
44 LPA
Highest package, FY24
Placement domain mix
Data Analytics
40%
Consulting
30%
Data Science & AI
20%
Software Dev
10%
Product
10%
Where we're strongest: data, analytics, and consulting roles for BFSI, GCCs, and Tier-1 consulting firms. Where we're growing fastest: deep-tech engineering for ServiceNow, Salesforce, and SAP ecosystems.
07 / For engineering leaders
Built for the people who actually have to ship with the hires.
Most hiring tools talk to CHROs. We also talk to your CTOs, EMs, and tech leads — because they're the ones who pay the cost of a bad hire.
What Mr Hire actually evaluates
CODE
Real production scenarios — debug a failing pipeline, design a microservice, optimise a slow query
Not LeetCode. Not whiteboard puzzles. Tasks your engineers actually do on Tuesday morning.
SYSTEM
Architecture and trade-off reasoning under realistic constraints
Sample task: design a fraud detection system for 10K TPS. Walk through latency, cost, failure modes.
DEPTH
Probing follow-ups generated dynamically based on their answer, not a script
If a candidate claims Kafka experience, the next question gets specific about partitioning strategy.
FIT
Stack alignment, working style, communication clarity, async vs in-office preference
Surfaced as signal — your EM still owns the call. Mr Hire just shows the work.
A real interview snippet
mr-hire.in / interview / round-2
# Mr Hire / Senior Data Engineer / Round 2
candidate: clustered table on user_id, joined on event_time which wasn't clustered. micro-partition pruning was useless. switched clustering to event_time, query went 4min → 8sec.
candidate: yes, QUERY_HISTORY view. ~62% reduction in credits. tradeoff: re-clustering adds ~3 credits/day.
# scoring: depth=9.2/10, specificity=high, signal=hireable
# flag → routed to human EM for final loop
08 / Honest comparison
Where Mr Hire wins. Where he doesn't.
In 2026, the most trustworthy thing you can do is admit what you're not. Here's the honest picture.
Mr Hire
AI screeners (Mercor, Karat)
Traditional staffing
Pre-trained, deploy-ready talent
Voice / async AI interviews
Median time-to-shortlist
100+ engineer cohorts
Best for ultra-high volume (1,000+)
Custom upskilling for your stack
Engineering-led evaluation
If you need 1,000+ identical hires/quarter and quality is secondary to throughput, we're honestly not the right fit. We'll tell you that on the first call.
09 / Data, security & responsible AI
What we promise. What we're working on. Plainly.
No asterisks. No vague assurances. Here's exactly where we are on every dimension.
Data residency
Candidate data stored in India. Per-client isolation. Custom DPA for enterprise clients.
In production
Encryption
AES-256 at rest. TLS 1.3 in transit. Quarterly key rotation.
In production
SOC 2 Type II
Audit window started Q1 2026. Targeting Type II report by Q4 2026. Type I controls in place today.
In progress
GDPR & DPDP
Compliant with India's DPDP Act 2023 and GDPR. Right-to-deletion within 30 days.
In production
Bias & fairness audits
Quarterly review of interview scoring across gender, region, and college tier. Reports for enterprise clients.
Quarterly
Human-in-loop
Every hire routes through a named MAS recruiter and your hiring manager. Mr Hire does not finalise offers.
By design
Right to challenge
Any candidate can request a human review of their Mr Hire scoring. Completed within 5 business days.
In production
Model transparency
Scoring rationale exposed to your team for every shortlisted candidate. No black-box decisions.
In production
10 / A note from the founder
ST
ST
Sajan Tonge
Founder & CEO, MAS
IIT BHU · 5 yrs MAS, full-time
"I've spent five years training engineers, watching them get hired, and watching some of them quit by month four. Most of those failures weren't because the candidate was bad. They were because the screening was lazy."
"We built Mr Hire because the rest of the market is racing to put more AI between you and the candidate. We went the other way — we put MAS between the candidate and the role, before either of you meet."
"You will not get a perfect agent. You'll get one that's honest about what it's good at, escalates the things it shouldn't decide alone, and is built on five years of placement data nobody else has."
"If that sounds useful, my email is below. If something on this page is overclaiming, tell me — I'll fix it."
sajan@myanalyticsschool.com
11 / Start somewhere
Three ways to begin.
Pick what matches your hiring problem.
12 / FAQ
The questions you're about to ask.
How is Mr Hire actually different from Mercor, Karat, or HireVue?
Those platforms are AI screeners — they evaluate candidates from the open market. Mr Hire was trained on candidates MAS itself developed and placed (20K+ engineers, 800+ placements, 250+ companies). When Mr Hire scores someone as deploy-ready, the benchmark is real placements that stuck. That ground truth is the moat — it's not something you can scrape together. We also run staffing operations, so we own the full loop from training to deployment, not just one slice.
How does the <7 day hiring process actually work?
Days 1–2: you upload the JD; Mr Hire parses it and builds a skill profile. Days 2–3: 1,000+ resumes screened in parallel, top 20–30 ranked. Days 4–5: AI voice interviews run 24/7 with real-world coding scenarios. Days 6–7: ranked shortlist with explainable scoring delivered to your hiring manager. You interview the top 3–5 and decide.
What does it cost?
Spot hires: 8.33–12% of first-year CTC depending on role (AI/ML at 12%, Backend/Data at 10%, Product/Consulting at 8.33%). Your first hire is free. Bench engagements: monthly billing per engineer, custom rates. Bulk cohorts: fixed-price per cohort, quoted on the role mix. No setup fee, no payment until a successful hire.
How does Mr Hire fit with our existing ATS?
Native integrations with Workday, Lever, SmartRecruiters, BambooHR, SAP SuccessFactors, and iCIMS. Custom APIs for everything else. Setup typically 48–72 hours. Candidate profiles, scores, transcripts, and offer letters all sync to your system of record.
Are the AI interviews really unbiased?
"Unbiased" is the wrong word — every model has bias; the question is whether you measure and correct for it. We do. Quarterly bias audits across gender, region, and college tier. Scoring rationale is exposed for every shortlisted candidate. Candidates can request a human review. We will not claim "completely unbiased" — that's a marketing lie. We will claim "actively measured, reviewed, and corrected."
What if Mr Hire isn't the right fit for us?
We'll tell you on the first call. If you need 1,000+ identical hires per quarter and quality is secondary to volume, we are honestly not your best option. We work best for specialised, high-stakes engineering and analytics roles where the cost of a bad hire is much higher than the cost of a good one.
