SDET with AI

Location: Austin, TX Salary: $50.00 USD Hourly - $55.00 USD Hourly Description: Our client is currently seeking a SDET with AI

Location: Onsite in Austin, TX or Southlake, TX

Job Type: 6 months with possibility of extension

Minimum qualifications:

Bachelor's degree in Computer Science, Software Engineering, Information Systems, a related technical field, or equivalent practical experience.

8 years of experience in software testing, test automation, software development, or quality engineering for highly available enterprise applications.

Hands-on experience designing, developing, and maintaining automated test frameworks using modern programming languages.

Experience with API testing, UI automation, integration testing, end-to-end testing, and regression automation.

Experience validating cloud-native, containerized, or service-based applications deployed on enterprise platforms (e.g., Kubernetes, Docker, Google Cloud Platform, AWS, Azure).

Experience with SQL or NoSQL databases, including test data creation, data validation, and quality checks.

Experience with CI/CD pipeline integration, source control, and collaboration tools (e.g., GitHub, GitLab, Azure DevOps, Jira).

Experience with messaging, streaming, or event-driven technologies (e.g., Kafka, RabbitMQ, cloud pub/sub services).

Demonstrated hands-on experience using Generative AI coding assistants (e.g., GitHub Copilot, Gemini Code Assist, Claude Code) across SDLC workflows for test generation, refactoring, and troubleshooting.

Understanding of object-oriented programming, data structures, debugging practices, and automation design principles.

Preferred qualifications:

Experience working as a Software Development Engineer in Test on an Agile Scrum, Kanban, or scaled Agile team.

Experience with performance, reliability, resiliency, observability, or production-readiness testing for high-volume distributed platforms.

Experience in the financial services, capital markets, or wealth management industries.

Experience defining automation strategy, framework standards, coding guidelines, and release readiness criteria across multiple teams.

Exposure to AI/ML implementation concepts, Large Language Models (LLMs), agentic workflows, or Retrieval-Augmented Generation (RAG).

Experience mentoring engineers or influencing teams on test automation, AI-assisted SDLC adoption, and continuous improvement practices.

Excellent problem-solving, critical thinking, and communication skills, with a bias for action and a collaborative mindset.

About the job

As a Senior Software Development Engineer in Test with a focus on AI, you will help define and execute technology-agnostic quality engineering practices across modern application stacks. You will partner with developers, architects, product owners, and operations teams to improve test automation, release confidence, and engineering productivity while responsibly applying AI-assisted SDLC practices.

This role is not limited to a single programming language or technology stack. You will apply quality engineering practices across modern enterprise platforms, demonstrating depth in automation design, test strategy, validation rigor, and production-quality engineering. You will be expected to continuously learn new tools, frameworks, cloud platforms, and AI-assisted engineering workflows as business needs evolve.

Responsibilities:

Partner with cross-functional teams to understand technical requirements and translate them into comprehensive test strategies, test cases, and release validation plans.

Design, develop, and maintain scalable automated test frameworks and suites across UI, API, integration, data, event-driven, and end-to-end layers.

Integrate automated tests into CI/CD pipelines to improve test reliability, reduce manual validation effort, and enable faster, safer releases.

Validate distributed, high-volume, and event-driven systems, focusing on data integrity, service contracts, resilience, and failure recovery scenarios.

Use AI-assisted engineering tools to improve test design, automation development, test coverage, defect analysis, and engineering productivity while maintaining ownership of correctness.

Apply prompt engineering, reusable instructions, and agentic concepts to accelerate quality engineering activities in a responsible and measurable manner.

Create and maintain quality artifacts, including test plans, automation strategies, defect analysis, validation evidence, and audit-ready documentation.

Influence quality outcomes across the SDLC through automation-first thinking, risk-based testing, and continuous improvement.

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