Sdet Automation Engineer Jobs (NOW HIRING)

The primary responsibility of the Senior Software Development Engineer in Test (AI-First Development) is to design, orchestrate, and validate the automated verification systems that gate every change shipped through the AI-First Software Development Lifecycle (SDLC). This role operates within an AI-First SDLC in which AI agents serve as primary producers of code, configuration, and test artifacts, while the Senior Software Development Engineer in Test (SDET) provides test strategy, context engineering, human-in-the-loop governance, and final accountability for the quality of delivered software. The Senior SDET is an experienced quality engineer or test automation engineer who has adopted modern AI-assisted development tools as a core part of their daily workflow and is prepared to grow into deeper agent orchestration, context engineering, and verification responsibilities.

Test Strategy, Agent Workflow Design, and Orchestration Design – build, and maintain AI agent workflows that produce unit, integration, end-to-end, performance, and security test suites from specifications and intent documents. Decompose acceptance criteria and technical contracts into discrete, verifiable test scenarios that AI agents can execute effectively within defined boundaries. Select and configure appropriate AI models, agent frameworks, and test‑generation tooling for each workflow based on risk level, coverage requirements, and cost considerations. Construct and maintain test context that provides agents with test patterns, fixture strategies, data classification rules, and domain information needed to produce correct and consistent test outputs. Contribute to the testing toolchain, including reusable test skills, automation hooks, and project memory files that provide persistent context across agent sessions. Authoring of advanced toolchain components may be developed on the job. Systematically capture defect patterns, escape modes, and verification failures from each development cycle and encode them back into shared context, test skills, and agent configurations so that subsequent work becomes more reliable. Participate in collaborative refinement sessions to align on acceptance criteria, technical contracts, and test context packages before agent execution begins.

Verification and Quality Assurance – operate the multi‑layer Verification Framework on every pull request, with primary ownership of the automated testing layer, validating functional correctness, security posture, performance characteristics, code quality, and regulatory compliance. Apply human oversight at governance checkpoints appropriate to the risk level of each workflow, including pre‑execution review, in‑flight observation, and post‑execution audit. Review, test, and approve AI‑generated code and test suites, ensuring they meet Sands testing standards, architectural guidelines, and security requirements before promotion to production. Verify that AI‑generated tests exercise specified intent rather than mirroring implementation, and reject suites that pass without actually exercising the behavior the specification asked for. Support independent QA verification after merge, contributing to system, integration, and regression testing in production‑like environments and partnering with the QA Lead on User Acceptance Testing coordination where applicable. Support agent observability practices that track test behavior, flakiness signals, coverage trends, and defect escape rates across workflows.

Test Engineering and Architecture – architect and deliver scalable test automation frameworks across web, API, mobile, and data layers using AI‑First methodologies as the primary development approach. Define test data strategies, test environment patterns, and verification contracts that serve as foundational context for agent‑driven test generation. Collaborate with cross‑functional teams including engineering, product, infrastructure, and security to translate business requirements into executable verification workflows. Coordinate with QA and engineering teams across global locations to ensure consistency in testing standards and verification practices. Write, debug, and refactor test code directly when agent outputs require manual intervention or when designing novel test approaches. Ensure delivered test automation meets enterprise standards for reliability, maintainability, observability, and operational readiness.

Continuous Improvement and Mentorship – evaluate emerging AI models, agent frameworks, and test automation tools to continuously improve verification effectiveness and output quality. Mentor team members on AI‑assisted testing practices, context engineering techniques, and verification methodologies. Document test patterns, prompt and context libraries, and lessons learned to build institutional knowledge. Participate in collaborative construction sessions, guiding agent execution in real time and coaching team members on effective test orchestration techniques.

Perform job duties in a safe manner. Attend work as scheduled on a consistent and regular basis. Perform other related duties as assigned.

• At least 21 years of age
• Proof of authorization to work in the United States
• Bachelor's degree in Computer Science, Software Engineering, or a related field, or equivalent professional experience
• Must be able to obtain and maintain any certification or license, as required by law or policy
• 5+ years of professional software development or test automation experience, including time in senior or lead positions where the candidate owned the design and delivery of non‑trivial verification systems
• Demonstrated daily use, over the past 6 months or more, of at least one modern AI‑assisted development tool such as Claude Code, Cursor, GitHub Copilot, or Windsurf, with the ability to speak concretely about effective usage patterns and failure modes
• Strong foundational knowledge in at least one major programming ecosystem (such as .NET/C#, JavaScript/TypeScript, Python, Java, or Go) and hands‑on experience with at least one modern test automation framework (such as Playwright, Cypress, REST Assured, pytest, or comparable)
• Familiarity with additional layers (UI, API, integration) is a plus
• Experience deploying and operating test automation on at least one major cloud platform (Azure, AWS, or GCP). Azure experience is a plus given the LVS footprint but is not required
• Working knowledge of DevOps practices and CI/CD pipelines, including familiarity with how automated test gates fit into a build pipeline
• Demonstrated experience conducting thorough code reviews, identifying defects in both human‑ and AI‑generated outputs, and providing constructive technical feedback
• Excellent written and verbal communication skills, with the ability to articulate technical decisions and trade‑offs to both technical and non‑technical stakeholders
• Strong interpersonal skills with the ability to communicate effectively and interact appropriately with management, other Team Members and outside contacts of different backgrounds and levels of experience

• Practical experience constructing structured context for LLMs, including prompt design, RAG pipelines, context window optimization, project memory files (such as CLAUDE.md or AGENTS.md), and integration with MCP servers
• Familiarity with tactical context management techniques such as plan mode, context editing, and multi‑session splitting
• Experience authoring reusable skills, configuring automation hooks, building custom MCP servers, or otherwise assembling agent toolchains that enable repeatable, production‑grade test workflows
• Exposure to one or more specialized testing approaches such as performance and load testing (K6, JMeter, Gatling), contract testing, mutation testing, or chaos engineering. Proficiency in any one is a plus; depth across all is not expected
• Knowledge of secure testing practices, OWASP guidelines, and SAST/DAST/SCA tooling, and experience working within a regulated industry such as gaming, finance, healthcare, or hospitality
• Understanding of data privacy and responsible AI principles
• Working knowledge of relational or non‑relational databases sufficient to model test data, design verification queries, and reason about query performance

Must be able to physically access assigned workspace areas with or without reasonable accommodation. Work remotely as necessary. Work indoors and be exposed to various environmental factors such as, but not limited to, CRT, noise, and dust. Utilize laptop and standard keyboard to perform essential functions of the job.