Job Description

A QA Engineer for AI Initiatives is responsible for ensuring the quality, reliability, fairness, and performance of AI/ML-powered products and systems. Unlike traditional QA, this role requires deep understanding of non-deterministic model behavior, data quality, and AI-specific failure modes such as hallucinations, bias, and model drift.

Key Responsibilities

• Design and execute test strategies specifically for AI/ML models, LLM-based applications, and data pipelines

• Develop automated test frameworks for model validation, regression testing, and performance benchmarking

• Evaluate model outputs for accuracy, consistency, relevance, hallucination, and bias across diverse inputs

• Test RAG (Retrieval-Augmented Generation) pipelines, chatbots, recommendation systems, and other AI-driven features

• Collaborate with data scientists and ML engineers to define acceptance criteria and quality thresholds

• Build and maintain evaluation datasets, ground truth sets, and adversarial test cases

• Monitor models in production for drift, degradation, and anomalous behavior

• Validate data quality, data pipelines, and feature stores that feed AI systems

• Document defects, edge cases, and failure patterns specific to AI behavior

• Ensure AI systems meet ethical, fairness, and compliance standards (bias audits, explainability checks)

Required Skills & Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field

• 3–6 years of QA experience, with at least 1–2 years in AI/ML quality assurance

• Strong proficiency in Python for test automation and data analysis

• Familiarity with LLM evaluation frameworks (e.g., RAGAS, DeepEval, Promptfoo, LangSmith)

• Hands-on experience with testing tools: Pytest, Selenium, Postman, or similar

• Understanding of ML lifecycle — training, validation, deployment, and monitoring

• Knowledge of data quality tools and pipeline testing (Great Expectations, dbt tests)

Nice to Have

• Experience with prompt engineering and red-teaming LLMs

• Familiarity with MLOps platforms (MLflow, SageMaker, Vertex AI) 

• Knowledge of vector databases and embedding quality evaluation

• Understanding of AI safety, responsible AI principles, and fairness frameworks

• Experience with A/B testing and shadow deployment strategies

Soft Skills

• Analytical and inquisitive mindset — comfortable challenging model outputs

• Ability to think like both a user and an adversary (red-team thinking)

• Strong documentation and communication skills

• Collaborative approach with data science, engineering, and product teams

• High attention to detail with a quality-first attitude