Objective Technical
Hiring & Placement
Infrastructure.
Automate technical screening, in-browser code execution sandboxes, and rubric-based qualitative evaluation.
1 def two_sum(nums, target): 2 seen = {} 3 for index, value in enumerate(nums): 4 complement = target - value 5 if complement in seen: 6 return [seen[complement], index] 7 seen[value] = index 8 return []
Next-Generation Proctored Evaluation Architecture
Seamlessly bridging campus talent with enterprise placement standards through live AI interviewing and transparent algorithmic scoring.
Dr. Elena Vance
AI InterviewerLead Architect • Distributed Systems & Concurrency
1. Live Multi-Avatar Proctored Session
Interactive real-time technical dialogue powered by 8 specialized AI interviewers with live voice synthesis, candidate audio transcription, and window focus tracking.
Yash Bardhan • Candidate #142
JMIT Radaur • Python Full-Stack & Systems
2. Cryptographic Qualitative Scorecards
Every assessment compiles full algorithmic efficiency analysis, memory complexity profiling, and recruiter sign-offs into exportable, tamper-proof JSON performance passports.
Institutional Placement Engine
Zero compromise on student fairness, data privacy, or proctoring rigor.
TPO Batch Placement Drives
Mass-screen hundreds of engineering students concurrently with unique 2-hour proctored tokens.
O(1) Memory & Complexity Profiling
Code runs against 14+ automated benchmark test cases measuring execution ms and RAM allocation.
NYC Law 144 & GDPR Compliant
Objective candidate scoring without discriminatory facial emotion tracking or algorithmic bias.
Welcome back, Yash Bardhan
Roll No: 1222151 • JMIT Radaur • Target: SDE-1 / Full-Stack & Systems
Join Corporate Assessment
Enter the 6-digit scheduled interview token issued by your college placement officer or employer recruiter to access the formal assessment room.
Start Mock Technical Interview
Step-by-step setup: Confirm your target track, select from 8 specialized AI Interviewers with voice auditioning, configure timer & difficulty, and launch your proctored test.
Verified Assessment History
Cryptographically Verified Passports| Track | AI Interviewer | Score | Verdict | Scorecard |
|---|---|---|---|---|
| Python Full-Stack & Concurrency | Dr. Elena Vance | 89.4 / 100 | Strong Advance | |
| Competitive DSA & Graphs | David Thorne | 92.0 / 100 | Strong Advance |
Technical Interview Session
Candidate: Yash Bardhan • Interviewer: Dr. Elena Vance
Dr. Elena Vance
Lead Technical Architect
Explain the trade-offs between a sliding window log rate limiter and a token bucket algorithm in distributed systems.
✓ Test Case 2 Passed in 6ms (O(1) memory)
Yash Bardhan • Technical Scorecard
Evaluated by Vettis EVAL.AI Engine · NYC Local Law 144 Compliant
Clear breakdown of distributed concurrency locks and token bucket rate limiting trade-offs.
Code passed all edge test cases with O(1) amortized space complexity.