Sr. Quality Automation Engineer

Authenticx•Indianapolis, IN

About The Position

The Senior Quality Automation Engineer is a hands-on role responsible for designing, building, maintaining, and integrating automated tests across Authenticx’s healthcare conversation intelligence platform. The engineer translates customer workflows, product requirements, user personas, and healthcare realities into automated coverage that protects important customer experiences. They own automation for critical product areas and prioritize coverage according to customer impact, product risk, release frequency, and business value. The role partners with Product, Engineering, Data Science, Implementation, and Customer teams to improve product quality, testability, and release confidence in a fast-paced startup environment. The position builds automation around real customer use cases across healthcare quality, coaching, compliance, operations, analytics, and customer experience workflows. It covers UI workflows, APIs, backend services, data pipelines, integrations, reporting, and AI-powered features, using appropriate combinations of UI, API, integration, and data-level testing. The engineer also integrates automated tests into CI/CD pipelines, troubleshoots test and environment failures, and validates AI-powered product experiences using repeatable scenarios and representative data.

Requirements

5+ years in test automation, SDET, or software engineering, with hands-on ownership of automated suites.
Production-level coding in at least one of TypeScript, JavaScript, C#, or Python.
Playwright or equivalent modern UI automation experience on complex web applications.
REST API testing experience (Bruno, Postman, or code-based), covering authentication, payload validation, and error handling.
SQL proficiency to validate data integrity and processing outcomes.
CI/CD integration experience, including failure triage, flaky test diagnosis, and test data management.
Ability to assess product risk and choose the right test layer (UI, API, or data) without having every case defined.
Experience validating asynchronous, multi-service workflows.

Nice To Haves

C#/.NET, PostgreSQL, and Azure experience.
Experience testing LLM or ML features using representative datasets, expected outcomes, and threshold-based regression checks.
Experience with ingestion pipelines, event-driven systems, and third-party integrations.
Healthcare, contact center, or regulated environment experience.
Experience using AI-assisted development tools for test creation and debugging.

Responsibilities

Learn how customers use the platform across healthcare quality, coaching, compliance, operations, analytics, and customer experience workflows.
Translate workflows, acceptance criteria, production issues, and known risk areas into meaningful automated test scenarios.
Determine what should be automated, the appropriate testing layer, and the level of coverage needed.
Balance UI, API, integration, and data-level testing to create reliable, maintainable coverage.
Design, write, maintain, and troubleshoot automated tests for UI workflows, APIs, backend services, data pipelines, integrations, reporting, and AI-powered features.
Build and maintain smoke, regression, acceptance, integration, and end-to-end test suites.
Use Playwright to automate critical user journeys and product workflows.
Use Bruno or Postman to validate APIs, payloads, integrations, authentication, error handling, and backend behavior.
Use SQL to validate data accuracy, workflow integrity, processing outcomes, reporting outputs, and customer-facing analytics.
Build reusable automation utilities, fixtures, helpers, and test data patterns.
Integrate automated tests into CI/CD pipelines to provide earlier and more reliable quality signals.
Investigate flaky tests, environmental issues, timing problems, and test-data failures, distinguishing them from product defects.
Identify high-risk coverage gaps and build automation to address them.
Work with Product, Engineering, Data Science, Implementation, and Customer teams to understand requirements, risks, and real-world usage.
Partner with developers to improve testability, observability, logging, test data setup, and API contracts.
Use logs, monitoring tools, database queries, API responses, and product data to investigate failures and validate system behavior.
Communicate defects, automation gaps, and release risks clearly.
Share automation patterns and testing practices that strengthen the broader quality engineering discipline.
Build automated validation for classifications, model outputs, prompt and configuration changes, structured responses, extraction workflows, and downstream behavior.
Develop repeatable test scenarios for deterministic and semi-deterministic AI behaviors.
Validate expected outputs, required fields, edge cases, failure conditions, and downstream workflow impacts.
Use representative test data and known expected outcomes to detect regressions.
Partner with Data Science and Engineering to improve the testability and regression coverage of AI-powered features.