Software Quality Assurance Engineer

7 days ago


Karachi, Sindh, Pakistan Saynt AI Full time 50,000 - 600,000 per year

QA Engineer (Remote) – Saynt AI (SMC Pvt Ltd)

Compensation:
PKR 50,000 / month

Hours:
4–5 hours per day

Location:
Remote

Department:
QA

About the Role

Saynt AI is hiring a
QA Engineer
who can
break things intelligently
, expose weaknesses in our AI and full-stack applications, and raise the engineering standard—not babysit developers. If you can't think critically, document accurately, or test systematically, this job isn't for you.

You will be responsible for ensuring the quality of:

– AI-powered automation platforms

– Full-stack web apps (React + FastAPI or backends)

– Multi-agent workflows

– Internal dashboards and client-facing systems

Responsibilities

Manual Testing:

– Functional, A/B testing

– End-to-end (E2E) workflow validation

– UI/UX consistency evaluation

– Testing of REST APIs, authentication flows, and user journeys

AI/Automation Testing:

– Validate AI outputs for correctness, consistency, edge cases

– Stress test agent workflows, RAG pipelines, and automation logic

Documentation:

– Write clear, precise bug reports with steps, screenshots, severity

– Create and maintain test cases, test plans, and release notes

– Maintain internal QA documentation for dev and AI teams

Collaboration:

– Work closely with frontend, backend, and AI teams

– Participate in dev standups

– Verify fixes before deployment

– Ensure no buggy PR ever reaches staging or production

RequirementsMust-Have Skills:

– Solid understanding of QA methodologies

– Strong analytical and problem-solving mindset

– Experience testing full-stack apps (React, REST APIs, databases)

– Basic understanding of Machine Learning & LLM behavior

– Ability to plan and execute test cycles independently

– Excellent documentation discipline

Nice-to-Haves:

– Familiarity with automation tools like Cypress, Playwright, Selenium

– Experience with Postman, Swagger, JMeter, or k6

– Exposure to AI testing (LLMs, RAG, embeddings, NLP models)



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