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Computer Vision Engineer — Smart Campus
Computer Vision Engineer — Smart Campus
CNV AI Labs Inc's logo

Computer Vision Engineer — Smart Campus

SHITAL BAINGNE's profile picture
Posted by SHITAL BAINGNE
3 - 6 yrs
₹6L - ₹10L / yr
Remote only
Skills
Computer Vision
skill iconDeep Learning
skill iconMachine Learning (ML)
skill iconPython
Image Processing
yolov7
ONNX
Artificial Intelligence (AI)
skill iconDocker
FastAPI
Cloud Computing
RESTful APIs
Linux/Unix

The problem you are solving

50,000 students. Hundreds of classrooms. Attendance marked on paper by a faculty member who has 90 seconds to call roll before lecture starts. It is inaccurate, gameable, and meaningless as data. We are replacing it with existing CCTV infrastructure — no new hardware, no wristbands, no apps. Just cameras that already exist, a model that recognises faces at scale in real-time, and a system smart enough to also tell you which rooms are overcrowded, which are empty, which have lights burning with nobody inside, and which need security attention. Campus intelligence, from cameras that are already there.


What you will build

Face recog.

Identify 50,000 enrolled students from CCTV feed — low-res, partial occlusion, varying angles


Attendance

Real-time auto-marking at scale, synced to the student ERP — no manual input


Crowd detects

Flag overcrowded rooms vs capacity threshold, real-time alerts to admin


Empty rooms

Detect booked-but-empty rooms and free them up automatically


Utilities

Lights on in empty room, fan running, AC on — flag for facilities team with camera snapshot


Edge + cloud

Low-latency processing at edge where needed, aggregation and analytics on cloud


Must have built something hard — any of these

Face recognition at 10,000+ identities

Real-time video analytics pipeline

YOLO / object detection production deploys

Multi-camera synchronization system

Edge inference (Jetson / Open VINO / ONNX)

Crowd / occupancy detection system


Competition — 3 rounds

Round 1

Given a set of low-res CCTV stills from a real campus: identify occupied vs empty rooms, count heads, detect lights-on-no-occupancy. Submit working code.


Round 2

48-hour sprint: extend your solution to handle a 10-camera simulated feed simultaneously. Latency under 2 seconds per frame is the bar.


Round 3

Live oral defense with Sachin and Hemant. Scale it to 500 cameras. Where does it break? How do you fix it?


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Shubham Vishwakarma

Full Stack Developer - Averlon
I had an amazing experience. It was a delight getting interviewed via Cutshort. The entire end to end process was amazing. I would like to mention Reshika, she was just amazing wrt guiding me through the process. Thank you team.
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About CNV AI Labs Inc

Founded :
2017
Type :
Product
Size :
100-500
Stage :
Profitable

About

N/A

Company social profiles

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Shubham Vishwakarma

Full Stack Developer - Averlon
I had an amazing experience. It was a delight getting interviewed via Cutshort. The entire end to end process was amazing. I would like to mention Reshika, she was just amazing wrt guiding me through the process. Thank you team.
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