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 role translates customer workflows, product requirements, user personas, and healthcare realities into automated coverage, owning automation for critical product areas and prioritizing coverage according to customer impact, product risk, release frequency, and business value. The engineer partners with Product, Engineering, Data Science, Implementation, and Customer teams to improve product quality, testability, and release confidence. The position includes testing UI workflows, APIs, backend services, data pipelines, integrations, reporting, and AI-powered features; integrating tests into CI/CD; investigating failures and coverage gaps; and validating AI-powered experiences using representative data and expected outcomes.

Requirements

At least 5 years in test automation, SDET, or software engineering, with hands-on ownership of automated suites.
Production-level coding experience in at least one of TypeScript, JavaScript, C#, or Python.
Experience with Playwright or equivalent modern UI automation on complex web applications.
REST API testing experience, using Bruno, Postman, or code-based approaches, covering authentication, payload validation, and error handling.
SQL proficiency for validating 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 select the appropriate 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 with 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.
Decide what to automate, select the appropriate testing layer, and determine the needed coverage.
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 create automation to address them.
Work cross-functionally 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 quality engineering across the organization.
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 testability and regression coverage of AI-powered features.