Job Description

Brief JD:

• Experience in designing LLM/RAG test automation solutions
• Experience in testing for bias, drift, and fairness.
• Familiarity with performance metrics (precision, recall, F1, ROC-AUC).
• Knowledge of MLflow MLOps framework
• Knowledge of tools for synthetic data generation and boundary testing
• Knowledge of Azure DevOps for CI/CD pipeline development
• Experience in RAG/pipeline evaluation frameworks -
  • pytest: Test automation framework
  • DeepEval or TruLens: LLM test assertions
  • RAGAS: RAG-specific metrics
  • Eleuther: LLM evaluation harness
  • Garak or Promptfoo: LLM red-teaming
  • Evidently: Drift/performance monitoring
• Exposure to explainability frameworks (SHAP, LIME, Captum)

Handson experience in API and Database testing.
Practical knowledge of Databricks, Azure Cloud services land distributed data validation.
Proficiency with Azure DevOps pipelines YAML templates, agent pools, CICD workflows.
Experience in implementing AIML practices in testing e.g., test generation, anomaly detection, log analysis to improve test efficiency and coverage.
Familiarity with Cucumber (BDD) and test reporting frameworks (e.g., Allure).
Strong understanding of integration testing across Databricks streaming jobs, applications and microservices.
Experience with performance testing tools (JMeter, LoadRunner, or equivalent).
Experience with Pester framework for validating PowerShell scripts and infrastructure automation.
Solid understanding of Agile/Scrum methodology and handson usage of JIRA.
Excellent problem-solving, debugging, and communication skills, with the ability to advise development teams on testing best practices.

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