Senior Developer, Artificial Engineering

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., * 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)

• 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