Software Engineer in Test

Job Description

**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.

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Role Overview

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We 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.

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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.

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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.

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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.

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What You’ll Do

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Quality for Your Features and Workstreams

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• Own the quality of features or workstreams assigned to you, from planning through release readiness
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• Define the testing approach for your area, including what needs testing, at what depth, and using which methods
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• Partner with developers and product managers on requirement reviews, design discussions, and sprint planning to catch issues early
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• Track quality signals for your area (defects, coverage gaps, escaped issues) and act on them
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Automation Framework Contribution

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• Build and maintain scalable automation that covers the scenarios that matter for your area
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• Design reusable automation framework components (helpers, utilities, page objects, shared services) that are scalable and reliable enough for the team to depend on
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• Contribute to and evolve the team's automation framework, following established patterns and shaping new ones where needed
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• Write clean, production-quality automation across UI and API layers using Playwright, Selenium, or equivalent tools
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• Keep the automation suite healthy. Fix flakes, improve reliability, and remove tests that no longer earn their place.
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AI Implementation in QA

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• Implement AI-assisted approaches within your area's QA workflow. Intelligent test generation, self-healing locators, automated step definitions, and AI-powered reporting.
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• Work hands-on with LLM APIs (Claude, OpenAI, or similar) to build utilities the team actually uses
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• Apply practical understanding of how large language models work (context windows, prompt structure, response handling) to build reliable integrations
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• Evaluate AI tools critically. Adopt what genuinely improves quality outcomes.
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Collaboration and Knowledge Sharing

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• Participate actively in code and design reviews. Give substantive feedback.
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• Document your work well enough that others can build on it
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• Support and pair with less experienced SDETs when needed
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Reporting and Execution

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• Produce test reports and quality summaries that stakeholders trust and act on
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• Support execution strategy improvements where they help. Parallelism, environment configuration, flakiness reduction.
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• Contribute to CI/CD pipeline integration if and when needed. This is not a heavy focus for us, so working knowledge is enough.
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Quality Engineering Foundation (Must Have)

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• Proven track record of owning quality for features or workstreams. Not just executing test cases handed to you.
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• Strong grasp of test strategy, risk-based coverage, and the tradeoffs between manual, automated, and exploratory testing
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• Experience partnering closely with developers and product managers
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• Comfortable making judgment calls on quality risk and communicating them clearly
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Automation (Must Have)

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• Strong hands-on experience with Playwright and/or Selenium WebDriver
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• Proficiency in TypeScript, Python, or C# (.NET). C# experience is a plus given our existing codebase.
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• BDD experience (Cucumber, Gherkin, and structured step definitions) is desirable. Familiarity with any major BDD framework is acceptable.
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• API testing proficiency alongside UI automation
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• Solid understanding of Page Object Model, component-driven design, and reusable test architecture
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AI in QA (Hands-On Experience Required)

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• You have built or integrated something using LLM APIs in a QA or automation context. Not just used AI as a productivity tool.
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• Comfortable with prompt engineering. Structuring prompts for test generation, defect summarization, or coverage analysis.
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• Working understanding of how large language models function, enough to design production-ready integrations that handle real-world conditions
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Data and Backend Skills (Must Have)

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• Strong SQL skills for querying application databases, validating data across layers, and setting up test data
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• Comfortable working with API testing beyond basic REST calls. Chaining requests, validating payloads, and handling authentication.
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Communication and Ownership

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• Clear communicator. You explain technical concepts to non-technical stakeholders and give real feedback in reviews.
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• Reliable at delivery time. You step up when a release is at risk.
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• Independent operator. You take direction and return with solutions.
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Engineering Ecosystem (Good to Have)

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• 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.
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• Familiarity with any test management tool (Azure Test Plans or equivalent)
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• Basic understanding of ETL processes and data pipelines
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• Basic understanding of Databricks or similar data platforms
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Qualifications

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• B.E. / B.Tech / M.Tech in Computer Science, IT, or related engineering discipline
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• 5+ years of QA and test engineering experience with strong hands-on framework development
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• Demonstrable 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.
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Primary Technologies

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• Test Automation: Playwright, Selenium WebDriver
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• Languages: TypeScript, Python, or C# (.NET)
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• API Testing: REST beyond basics: request chaining, payload validation, authentication
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• Data: Strong SQL for application databases, data validation, and test data setup
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• AI in QA: Hands-on with LLM APIs (Claude, OpenAI, or similar); prompt engineering
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• Test Management: Azure Test Plans
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• BDD (Optional): Cucumber, Gherkin, structured step definitions
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• CI/CD (Good to Have): Jenkins, GitHub Actions, or Azure DevOps. Basic working knowledge is enough.
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• ETL (Good to Have): Basic understanding of ETL processes and data pipelines
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• Databricks (Good to Have): Basic understanding of Databricks or similar data platforms
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