QA Analyst with AI

Tekskills; Bulandshahr; Hybrid; INR 800,000 - 1,100,000; Full time

Job summary

Tekskills seeks a QA Associate Engineer (AI Testing) with 4–7 years of QA experience to join our AI-focused testing team in India.

The role covers LLM behavior testing, prompt engineering, data pipeline testing, and CI/CD integration, with on-site/hybrid work across Pune, Hyderabad, or Bangalore depending on project.

Qualifications

• 4+ years of QA experience in AI-focused software testing.
• Hands-on with LLM behaviour, hallucinations and bias handling.
• Proficient in prompt engineering for adversarial/robustness tests.
• Experience verifying AI-driven QA like photo validation and recognition tests.
• Experience integrating LLM test suites into CI/CD (Azure DevOps, GitHub Actions).
• Knowledge of data pipeline testing and synthetic data generation.

Responsibilities

• Design and execute AI QA test plans.
• Develop LLM test cases and guardrails.
• Validate data pipelines and synthetic data.
• Collaborate with engineering to triage defects.

Skills

LLM testing

Prompt engineering

Test data management

CI/CD testing

AI QA knowledge

Defect triage

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.