Job Title : QE Lead AI & Analytics.

Location : Pune / Nashik, India.

Experience Required : 8+ Years.

Employment Type : full-time.

The job :

As a QE Lead AI & Analytics, you will define and lead the quality strategy for AI-driven and analytics platforms, ensuring trust, reliability, and measurable value for customers.

Youll work closely with data scientists, AI engineers, product managers, and platform teams to validate complex AI systems such as RAG pipelines, ML models, and analytics workflows.

This role is critical in shaping how we test, observe, and continuously improve AI systems in production, while mentoring teams and setting modern QE standards.

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 for AI systems, 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.

Essential Requirements :

- Proven experience in quality engineering with exposure to AI/ML-based applications and analytics platforms.

- Hands-on experience testing AI systems such as RAG, 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.

Desired Skills And Competencies :

- Strong technical curiosity and learning mindset, with a passion for emerging AI technologies (Curiosity, Aspiration).

- Ability to create measurable impact by improving AI reliability, trust, and customer outcomes (Impact).

- Experience with observability tools, logging, metrics, and monitoring for AI/ML systems.

- Excellent communication and collaboration skills, building trusted relationships across engineering, data, and product teams (Trust).