Senior Test Automation AI Engineer (Automation & Operations) - Vice President - Dallas

Goldman Sachs • Dallas, TX

1d • Onsite

About The Position

In the rapid development landscape of 2026, the role of a Senior AI/ML Engineer in test automation is to transform Quality Assurance (QA) from a reactive bottleneck into a proactive, intelligent layer. By leveraging Large Language Models (LLMs) and agentic workflows, you will build a "self-healing" test harness that provides the confidence needed for continuous, high-velocity deployments.

Requirements

• Experience with Large Language Models (LLMs) and agentic workflows.
• Proficiency in designing and maintaining test frameworks.
• Experience with AI for automated test updates (locators, scripts).
• Familiarity with frameworks like LangGraph or CrewAI for agentic workflows.
• Experience in analyzing Jira stories, PR diffs, and system architecture for test generation.
• Knowledge of ML for anomaly detection and predictive risk analysis in telemetry pipelines.
• Experience with Generative AI for synthetic data creation.
• Familiarity with automated evaluation frameworks (e.g., Giskard, DeepEval).
• Experience in architecting intelligent gates within CI/CD pipelines.
• Understanding of predictive test selection.
• Experience in cross-functional collaboration with developers and data scientists.
• Knowledge of building testability into AI models and microservices.

Responsibilities

• Design and maintain "self-healing" test frameworks that use AI to automatically update locators and scripts when UI or API schemas change, reducing maintenance toil by up to 70%.
• Implement agentic workflows (using frameworks like LangGraph or CrewAI) to analyze Jira stories, PR diffs, and system architecture to generate comprehensive test suites, including edge cases and negative scenarios.
• Build telemetry pipelines that use ML for anomaly detection and predictive risk analysis, identifying high-risk code areas before they reach production.
• Leverage Generative AI to create high-fidelity, privacy-compliant synthetic datasets for complex integration and performance testing.
• Establish automated evaluation frameworks (e.g., Giskard, DeepEval) to measure the accuracy, safety, and hallucination rates of AI-driven features.
• Architect intelligent gates within the CI/CD pipeline that use predictive test selection to run only the most relevant tests for a given code change, optimizing execution speed.
• Partner with developers and data scientists to ensure "testability" is built into AI models and microservices from the design phase.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed