Posted 10 days ago
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**Senior Test Automation AI Engineer (Automation & Operations)** in Dallas seeks a strategic leader to revolutionize Quality Assurance using AI and machine learning. Key responsibilities include: - **Autonomous Test Harness Engineering**: Design and maintain "self-healing" test frameworks that use AI to reduce maintenance toil by up to 70%. - **LLM-Powered Test Generation**: Implement agentic workflows to analyze Jira stories and generate comprehensive test suites, including edge cases and negatives. - **AI-Driven Observability & Monitoring**: Build ML pipelines for anomaly detection and predictive risk analysis to identify high-risk code areas before they reach production. Requirements: - **Proven experience** in test automation, AI, and engineering leadership roles. - **Expertise** in Large Language Models (LLMs) and agentic workflows (e.g., LangGraph, CrewAI). - **Familiarity** with synthetic data generation and AI evaluation frameworks (e.g., Giskard, DeepEval). - **Proficiency** in CI/CD pipeline integration and predictive test selection. - **Ability** to collaborate cross-functionally with developers and data scientists to ensure "testability" in AI models and microservices from the design phase. This **Vice President role** based in Dallas demands a passion for driving AI innovation in test automation and a commitment to delivering high-quality solutions in a fast-paced environment.
- Compensation
- Not specified
- City
- Dallas
- Country
- United States
Currency: Not specified
Full 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:
- 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.
