QA Analyst with AI

Tekskills; Hyderabad; Hybrid; INR 700,000 - 1,100,000; Full time

Job summary

Tekskills is seeking a QA Associate Engineer (AI Testing) to help ensure robust AI outputs and track quality across AI systems. You will design and execute tests focused on LLM behavior, data integrity, and prompt robustness.

In this role, you will integrate LLM test suites into CI/CD pipelines (Azure DevOps, GitHub Actions) and collaborate with engineering to triage defects. On-site or hybrid arrangements apply in India depending on project.

Qualifications

• 4+ years of QA experience with AI solutions.
• Experience with LLM behavior, hallucinations, bias, and edge cases.
• Experience in prompt engineering for adversarial and robustness tests.
• Knowledge of testing data pipelines and data coverage.
• Experience integrating LLM test suites into CI/CD (Azure DevOps, GitHub Actions).
• Experience in generating synthetic data and auditing data coverage.
• Documenting defects and supporting triage with engineering teams.

Responsibilities

• Design and execute AI-focused QA test plans.
• Evaluate LLM outputs for accuracy and safety.
• Create and maintain LLM test cases and guardrails.
• Integrate test suites into CI/CD pipelines.
• Collaborate with data and engineering teams to fix defects.
• Manage test data and synthetic data generation.

Skills

LLM testing

Prompt engineering

CI/CD testing

Data integrity testing

Test data management

Tools

Azure DevOps

GitHub Actions

Job description

We are currently hiring for a QA Testing Engineer AI Focused role, and your profile seems to match our requirements.

Please find the job details below:

Position: QA Associate Engineer (AI Testing)

Experience: 4 to 7 Years

Location: Pune / Hyderabad / Bangalore (On-site/Hybrid – as per project)

Job Description – QA Associate Engineer

Minimum Experience:

• At least 4 years of experience in software quality assurance, with a focus on AI solutions.

Must Have Hands-On Experience In:

• Deep understanding of LLM behaviour, including hallucinations, bias, and edge-case handling.
• Prompt engineering to design adversarial and robustness test cases.
• Conducting photo validation and recognition tests to ensure accuracy in AI-driven quality audits.
• Integrating LLM-focused test suites into CI/CD pipelines (Azure DevOps, GitHub Actions).
• LLM test case generation, RAG evaluation, and use of guardrails for secure, explainable testing.
• Strong background in data pipeline testing, ensuring input/output integrity of training and retrieval systems.
• Managing test data, generating synthetic data, and auditing data coverage.
• Documenting defects clearly and supporting triage with engineering teams.