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saad2003

Muhammad Aamir

@saad2003

AI Software QA Engineer

Pakistan
Englisch, Urdu
Einige Informationen werden in englischer Sprache angezeigt.
Über mich
I am an AI Quality Assurance Engineer with over 7 years of software testing experience and extensive expertise in manual testing, API testing, UI/UX validation, and automation. Recently, I have specialized in AI Quality Assurance, where I test AI agents and Large Language Model (LLM) applications. My experience includes validating AI prompts, contextual memory, prompt rewriting, conversation flows, RAG-based knowledge systems, and response accuracy while designing comprehensive positive, negative, and edge-case test scenarios. I am proficient with Jira, Postman, Selenium, and Agile. ... Mehr lesen

Kompetenzen

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saad2003
Muhammad Aamir
offline • 

Meine Dienstleistungen

Qualitätssicherung
I will do software QA for you in 24 hrs
Ab10 $ / ProjektWeitere Details
Datenformatierung und -bereinigung
I will covert pdf to excel in 24 hrs
Ab5 $ / ProjektWeitere Details

Portfolio

Arbeitserfahrung

AI Software QA Engineer

Not Found • Vollzeit

Oct 2018 - Present • 7 yrs 9 mos

AI Quality Assurance & AI Skills Validation (2025 – Present) Conduct end-to-end validation of AI agents across multiple industries, including: Technology & Software Engineering Financial Services Food & Hospitality Construction Legal Telecommunications Healthcare Retail & E-commerce Test hundreds of AI skills, prompts, and workflows for functionality, accuracy, and reliability. Design positive, negative, edge-case, and regression test scenarios for AI-powered applications. Validate contextual memory, prompt rewriting, conversation continuity, hallucination detection, and response consistency. Perform functional, usability, and domain-specific testing of AI agents. Create comprehensive test cases, bug reports, and validation documentation. Verify AI responses against expected business requirements and industry standards. Collaborate with AI engineers and product teams to improve prompt quality and model performance. Test Retrieval-Augmented Generation (RAG) knowledge bases across various industries. Evaluate AI-generated content for correctness, relevance, safety, and completeness.