QA Engineer | Video Analytics

Samriddhi Automations Pvt. Ltd.; Hinoba-an; On-site; PHP 420,000 - 720,000; Full time

Job summary

Samriddhi Automations Pvt. Ltd. seeks a Software QA Engineer to join the Video Analytics team and own quality across AI accuracy, functional testing, integration, performance and field validation.

You will build test strategies, automation and datasets to ensure releases perform reliably on real cameras, in real conditions and at scale, with hands-on testing of CCTV/IP camera workflows and NVR/VMS integrations.

Qualifications

• 2–5 years of software QA experience with video/CCTV/VMS or AI/ML products.
• B.Tech/BE/MCA in CS/IT/Electronics or equivalent.
• Strong QA methodology knowledge and Agile/Scrum practices.
• Python scripting for test automation.
• Experience with API testing (Postman/pytest).
• Familiarity with Linux, Docker, ONVIF/IP cameras.

Responsibilities

• Define and execute functional, regression, integration, performance, and security testing for video analytics modules.
• Build ground-truth datasets and measure precision, recall, false-alarm rate, ANPR read rate and latency.
• Build repeatable automation using Python, pytest, Postman and REST APIs.
• Validate FPS, resource usage, thermal behaviour, memory leaks and long-run stability on edge devices and GPU servers.
• Test NVR/VMS and ONVIF cameras, and integrate field issues into regression datasets.

Skills

Python scripting

API testing

Linux command line

Docker

ONVIF/RTSP

Video analytics testing

ML evaluation metrics

Education

B.Tech/BE/MCA in CS/IT/Electronics or equivalent

Tools

Postman

pytest

Job description

Software QA | AI Testing | Video Analytics

2–5 years

Software QA | AI Testing | Video Analytics

Software QA | AI Testing | Video Analytics

reporting_to

AI Product Manager / Head of Software VA

work_nature

Quality-focused / Automation / AI Testing

Where this role sits

Sparsh's Software & Video Analytics (VA) team builds AI-driven analytics for ANPR, face recognition, people and vehicle analytics, intrusion and perimeter detection.

This role sits within the VA team and owns quality across AI accuracy, functional testing, integration, performance and field validation.

You will build test strategies, automation and datasets that ensure every release performs reliably on real cameras, in real conditions and at scale.

The role

This role is:

Software QA AI Testing Test Automation Video Analytics Quality-focused

What you’ll work on

Video Analytics Testing

Define and execute functional, regression, integration, performance, stress and security testing for video analytics modules

Build ground-truth datasets and measure precision, recall, false-alarm rate, ANPR read rate, face recognition match rate and latency

Build repeatable automation using Python, pytest, Postman, REST APIs and video-replay or stream-simulation setups

Validate FPS, resource usage, thermal behaviour, memory leaks and long-run stability on edge devices and GPU servers

Integration & Field Testing

Test NVR/VMS, ONVIF cameras, alert channels and third-party integrations while converting field issues into regression cases and datasets

Who this role is for

• 2–5 years of software QA experience with hands-on testing of video, CCTV/VMS, IoT or AI/ML products
• B.Tech/BE/MCA in Computer Science, IT, Electronics or equivalent
• Strong understanding of QA methodology, test-case design, defect lifecycle and Agile/Scrum practices
• Python scripting experience for test automation
• Experience with API testing using Postman, pytest or requests
• Familiarity with Linux command line, Docker, IP cameras, RTSP/ONVIF, video codecs and NVR/VMS platforms
• Understanding of ML evaluation metrics including precision, recall, mAP and confusion matrix

What you’ll learn here

• How AI accuracy is validated using real-world video datasets and ground-truth data
• How video analytics products are tested across day/night, weather, camera angles and Indian scene conditions
• How automated testing is built for multi-camera video analytics systems
• How AI products are tested across edge devices, GPU servers, APIs, cameras and NVR/VMS platforms
• How field issues are converted into regression tests, datasets and product quality improvements
• Work on India's first STQC-certified, Made-in-India surveillance platform deployed at national scale
• Test AI that runs in the real world on live camera streams and in the field
• Gain exposure to AI, computer vision, software, hardware and video analytics under one roof

Ready to make AI reliable in the real world?