AI QA & Evaluation Engineer

Job Summary

We are looking for a skilled QA Evaluation Engineer to join our team. The role blends strategic QA leadership with hands-on technical validation and structured evaluation to safeguard the accuracy, reliability, compliance, and ethical use of AI models. You will partner across IT and Engineering teams to identify, design, implement and run robust testing frameworks and evaluation rubrics for a portfolio of GenAI solutions that will be used across our organization.

Responsibilities

• Be a primary contributor to our AI strategy, helping validate and test AI infrastructure, custom solutions and third-party SaaS offerings.
• Test Strategy Execution: Design and implement comprehensive test strategies for AI/ML systems, including accuracy, bias, robustness, and regression testing.
• Rubric-Based Evaluation: Design and implement self-contained evaluation tasks, including prompts, supporting files, and detailed grading rubrics to assess AI performance on functional workflows.
• Automation CI/CD: Automate validation suites for agentic/multi-agent systems, integration testing, and CI/CD pipelines for ML models.
• Data Validation: Validate that AI/ML models are consuming accurate, authorized, and properly structured data sources; ensuring data quality across training and inference.
• Observation Reporting: Meticulously observe and document AI agent behaviors, producing crisp, precise summaries and reports on model performance and hallucinations.
• Output Grounding: Validate prompt engineering outputs from a data accuracy standpoint, ensuring responses are grounded in verified data sources.
• Refinement Iteration: Iterate and refine evaluation tasks and rubrics based on feedback and team collaboration to ensure robust benchmarking methodologies.
• Security Governance: Ensure all AI data sources and structures meet governance, regulatory, and compliance standards, while implementing best practices for security and data privacy.
• Collaborate with teams from different areas. These areas include IT Engineering, IT Operations, Data Integrations, PMO, CRM, Risk Compliance, and business technology.
• Stay current on the latest work in AI and make technical recommendations to the organization.

Qualifications

• Proficiency in Python, TypeScript, or other programming languages used in AI and test automation.
• Proven skill in designing or applying rubric-based evaluation, grading against set criteria, or building structured scoring frameworks.
• Direct experience with LLM evaluation frameworks and benchmarking tools such as LangSmith, Confident AI, etc.
• Knowledge of the GenAI stack and solutions including Retrieval Augmented Generation (RAG).
• LLMs: Azure OpenAI, Vertex AI, ChatGPT Enterprise or similar.
• High attention to detail and ability to notice subtle patterns or inconsistencies (such as data hallucinations or logic errors) that others might miss.
• Advanced written communication skills, especially for documenting nuanced observations and feedback.
• Experience with Cloud platforms (Azure, GCP, AWS).
• Thorough understanding of DevOps/automation/CI/CD tools: GitHub, Terraform.
• Comprehension with AI ethics, risk management, and data governance.