What are the responsibilities and job description for the Senior Test Automation AI Engineer (Automation & Operations) - Vice President - Dallas position at Goldman Sachs & Co.?
Job Description
Role Overview:
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.
Responsibilities:
Role Overview:
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.
Responsibilities:
- Autonomous Test Harness Engineering: 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%.
- LLM-Powered Test Generation: 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.
- Intelligent Observability & Monitoring: Build telemetry pipelines that use ML for anomaly detection and predictive risk analysis, identifying high-risk code areas before they reach production.
- Synthetic Data Orchestration: Leverage Generative AI to create high-fidelity, privacy-compliant synthetic datasets for complex integration and performance testing.
- "LLM-as-a-Judge" Implementation: Establish automated evaluation frameworks (e.g., Giskard, DeepEval) to measure the accuracy, safety, and hallucination rates of AI-driven features.
- CI/CD Integration: 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.
- Cross-Functional Collaboration: Partner with developers and data scientists to ensure "testability" is built into AI models and microservices from the design phase.