Software Engineer in Test

**Candidates should be eligible to work for any employer in the United States without needing Visa sponsorship. Only considering W2 eligible candidates, C2C/1099 will not be considered.Role OverviewWe are looking for a Senior SDET who combines strong automation engineering with hands-on experience applying AI in a QA context. In this role, you own the quality of features and workstreams assigned to you, build reusable automation the team can rely on, and bring AI-assisted approaches into your day-to-day work.This is a senior individual contributor role. You take a technical direction and work through it independently, delivering working solutions with minimal hand-holding. You know when to ask clarifying questions and when to move.You will work closely with developers, product managers, and other QA engineers to understand what quality means for your area, define the right testing approach, and deliver measurable coverage improvements using a mix of automation and AI-assisted approaches.If you enjoy solving hard technical problems, delivering quality that people can trust, and building your AI in QA skills through real work, this role is for you.What You’ll DoQuality for Your Features and WorkstreamsOwn the quality of features or workstreams assigned to you, from planning through release readinessDefine the testing approach for your area, including what needs testing, at what depth, and using which methodsPartner with developers and product managers on requirement reviews, design discussions, and sprint planning to catch issues earlyTrack quality signals for your area (defects, coverage gaps, escaped issues) and act on themAutomation Framework ContributionBuild and maintain scalable automation that covers the scenarios that matter for your areaDesign reusable automation framework components (helpers, utilities, page objects, shared services) that are scalable and reliable enough for the team to depend onContribute to and evolve the team's automation framework, following established patterns and shaping new ones where neededWrite clean, production-quality automation across UI and API layers using Playwright, Selenium, or equivalent toolsKeep the automation suite healthy. Fix flakes, improve reliability, and remove tests that no longer earn their place.AI Implementation in QAImplement AI-assisted approaches within your area's QA workflow. Intelligent test generation, self-healing locators, automated step definitions, and AI-powered reporting.Work hands-on with LLM APIs (Claude, OpenAI, or similar) to build utilities the team actually usesApply practical understanding of how large language models work (context windows, prompt structure, response handling) to build reliable integrationsEvaluate AI tools critically. Adopt what genuinely improves quality outcomes.Collaboration and Knowledge SharingParticipate actively in code and design reviews. Give substantive feedback.Document your work well enough that others can build on itSupport and pair with less experienced SDETs when neededReporting and ExecutionProduce test reports and quality summaries that stakeholders trust and act onSupport execution strategy improvements where they help. Parallelism, environment configuration, flakiness reduction.Contribute to CI/CD pipeline integration if and when needed. This is not a heavy focus for us, so working knowledge is enough.Quality Engineering Foundation (Must Have)Proven track record of owning quality for features or workstreams. Not just executing test cases handed to you.Strong grasp of test strategy, risk-based coverage, and the tradeoffs between manual, automated, and exploratory testingExperience partnering closely with developers and product managersComfortable making judgment calls on quality risk and communicating them clearlyAutomation (Must Have)Strong hands-on experience with Playwright and/or Selenium WebDriverProficiency in TypeScript, Python, or C# (.NET). C# experience is a plus given our existing codebase.BDD experience (Cucumber, Gherkin, and structured step definitions) is desirable. Familiarity with any major BDD framework is acceptable.API testing proficiency alongside UI automationSolid understanding of Page Object Model, component-driven design, and reusable test architectureAI in QA (Hands-On Experience Required)You have built or integrated something using LLM APIs in a QA or automation context. Not just used AI as a productivity tool.Comfortable with prompt engineering. Structuring prompts for test generation, defect summarization, or coverage analysis.Working understanding of how large language models function, enough to design production-ready integrations that handle real-world conditionsData and Backend Skills (Must Have)Strong SQL skills for querying application databases, validating data across layers, and setting up test dataComfortable working with API testing beyond basic REST calls. Chaining requests, validating payloads, and handling authentication.Communication and OwnershipClear communicator. You explain technical concepts to non-technical stakeholders and give real feedback in reviews.Reliable at delivery time. You step up when a release is at risk.Independent operator. You take direction and return with solutions.Engineering Ecosystem (Good to Have)Familiarity with CI/CD pipelines (Jenkins, GitHub Actions, or Azure DevOps). Good to have, not a hard requirement. We are not heavily invested in CI/CD, so basic working knowledge is enough.Familiarity with any test management tool (Azure Test Plans or equivalent)Basic understanding of ETL processes and data pipelinesBasic understanding of Databricks or similar data platformsQualificationsB.E. / B.Tech / M.Tech in Computer Science, IT, or related engineering discipline5+ years of QA and test engineering experience with strong hands-on framework developmentDemonstrable experience implementing AI solutions in a testing or software engineering context. Experimental projects count if you can show what you built and what it delivered.Primary TechnologiesTest Automation: Playwright, Selenium WebDriverLanguages: TypeScript, Python, or C# (.NET)API Testing: REST beyond basics: request chaining, payload validation, authenticationData: Strong SQL for application databases, data validation, and test data setupAI in QA: Hands-on with LLM APIs (Claude, OpenAI, or similar); prompt engineeringTest Management: Azure Test PlansBDD (Optional): Cucumber, Gherkin, structured step definitionsCI/CD (Good to Have): Jenkins, GitHub Actions, or Azure DevOps. Basic working knowledge is enough.ETL (Good to Have): Basic understanding of ETL processes and data pipelinesDatabricks (Good to Have): Basic understanding of Databricks or similar data platforms