QE Lead - AI & Analytics

About the role

As a QE Lead – AI & Analytics you will define and drive the quality strategy for AI‑driven and analytics platforms, ensuring trust, reliability and measurable value for customers. You will work closely with data scientists, AI engineers, product managers and platform teams to validate complex AI systems such as RAG pipelines, LLM‑based applications and predictive models.

Key responsibilities

• Own and define the QA strategy for AI and analytics solutions, including ML models, RAG pipelines and data‑driven systems.
• Design and execute testing approaches covering functional, non‑functional, bias, robustness and reliability aspects.
• Establish observability, evaluation and monitoring practices for AI quality in pre‑production and production environments.
• Lead and contribute to test‑automation frameworks for AI, APIs, data pipelines and analytics workflows.
• Collaborate with cross‑functional teams to embed quality early in the AI development lifecycle.

Required profile

• Proven experience in quality engineering with exposure to AI/ML‑based applications and analytics platforms.
• Hands‑on experience testing AI systems such as RAG pipelines, LLM‑based applications or predictive models.
• Strong understanding of manual and automated testing approaches for APIs, data pipelines and distributed systems.
• Experience with AI evaluation techniques, metrics and validation strategies (accuracy, relevance, drift, hallucination, etc.).
• Ability to define QA standards, mentor engineers and influence quality practices across teams.

Required skills

• AI/ML testing
• RAG pipeline validation
• LLM‑based application testing
• Predictive model testing
• API testing
• Data pipeline testing
• Distributed systems testing
• Test‑automation framework development
• Observability and monitoring for AI systems
• AI evaluation metrics (accuracy, relevance, drift, hallucination)