Principal Quality Assurance Engineer

Location: Pune 

Summary

We are seeking a visionary and experienced Principal QA Engineer to architect, implement, and lead the quality assurance strategy for our cutting-edge AI-driven solutions on Project Mara. This role requires deep expertise in both traditional and AI/ML testing methodologies, with a proven ability to mentor a team, define technical direction, and ensure the delivery of high-quality, reliable, and ethical AI systems that drive business value.

Key Leadership & Strategic Responsibilities

• Define AI QA Strategy: Own, define, and continuously evolve the end-to-end AI/ML testing strategy, roadmaps (including model testing, data quality, and MLOps pipeline validation) aligned with the overall product and engineering vision.
• Leading Capabilities & Mentorship: Serve as a lead and mentor for QA team, establishing best practices for testing AI/ML workflows. Drive knowledge sharing and skill development within the team.
• Cross-Functional Quality: Collaborate with Developers, ML Engineers, and Product Owners to embed quality assurance practices from the design phase (Shift-Left), ensuring quality is prioritized across the entire Software/Model Development Life Cycle.
• Define and Assist Test Automation: Support readiness and maintain a robust, scalable API and End-to-End Test Automation for faster execution using Python, Java, or similar languages, specifically tailored for validating AI services and microservices.
• Research and Innovation: Lead the evaluation and adoption of emerging QA tools and ML testing frameworks to significantly boost test efficiency, coverage, and team capabilities.

Core AI/ML Quality Assurance Expertise

• Model Validation and Robustness: Architect and implement rigorous testing protocols to validate model accuracy, performance, statistical robustness, drift detection, and fairness (e.g., bias testing) to ensure reliable and business-aligned AI outcomes.
• AI/ML Testing: Own and capable to evolve AI/ML testing strategy, plans, and frameworks aligned with QA roadmap. Validate model accuracy, robustness, and fairness to ensure reliable, business-aligned results utilizing both manual and automated testing to assess AI-driven workflows.
• System Integration Testing: Develop and execute advanced API and microservices testing using tools like Postman, RestAssured, or similar frameworks, focusing on performance, load, and security aspects relevant to high-throughput AI services.
• Data Quality Assurance: Lead data validation efforts across the entire ML data lifecycle—to ensure input quality, integrity, statistical distribution, and overall model reliability.
• Debugging and Analysis: Apply expert-level analytical, debugging, and root-cause analysis skills to complex issues within AI workflow drive continuous quality improvement.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related quantitative field.
• 5 - 7 years of experience in software testing/QA, with a minimum of 3+ years dedicated to testing complex AI/ML systems and data pipelines in a senior or principal capacity.
• Proven experience mentoring, leading, or providing technical direction to a QA or Automation team.
• Hands-on expertise in designing and scaling automated test frameworks using languages such as Python (preferred), Java, or similar.
• Exceptional understanding of ML model lifecycle, MLOps practices, and specialized AI/ML testing methodologies.

• Excellent communication skills

• Expert-level knowledge of QA methodologies, SDLC/MDLC, and Agile/Scrum.
• Proficiency with API testing tools (e.g., RestAssured, Postman) and version control systems (Git).