Description

You will own the end-to-end quality strategy for securitization platforms and AI-driven systems, ensuring data integrity, model reliability, and release stability.

This role is on-site.

Responsibilities

• Design and execute comprehensive test strategies for AI models, including prompt engineering, bias/safety testing, and hallucination detection.
• Validate RAG pipelines and vector databases, testing edge cases like embedding drift, similarity search thresholds, and data quality.
• Build and maintain automated test suites using Java and Python, integrating with Jenkins and GitHub for CI/CD pipelines.
• Lead defect triage, coordinate regression testing for platform releases, and provide production support during critical processing windows.
• Mentor junior QA members and define quality metrics such as defect density and automation coverage.

Required Skills

• 7+ years of QA or quality engineering experience, with at least 3 years in a lead or senior capacity.
• Proficiency in Java and Python with demonstrated experience in framework implementation.
• Hands-on experience with test automation tools such as Selenium, Playwright, or Robot Framework.
• Strong understanding of LLM architecture, including tokenization, embeddings, and inference mechanisms.
• Experience with LangChain and LangGraph for testing chain/graph construction and failure points.
• Proficiency in API automation, backend validation, and database query development for reconciliation testing.
• Familiarity with CI/CD practices using Jenkins and GitHub.
• Knowledge of vector databases, RAG pipelines, and integration testing tools.

Key Skills

Education

Any Gradute