AI-Native Software Engineer, QA Automation

Location: Remote - India

Type: Full-time

Working Hours: All meetings and live collaboration happen between approximately 6:00 PM and 12:00 AM IST, overlapping US Pacific Time. Attendance in that window is required. Your remaining work is yours to schedule.

Commitment: Approximately 50 hours per week

Industry: Healthcare technology experience is preferred but not mandatory

About the Company

Zenara Health is a technology-oriented mental healthcare organization building clinical software for behavioral-health clinics. Our products include Assess and Zenflow, which support assessments, care management, clinical workflows, and operational delivery .

We are a startup, not a department. We are building an AI-native engineering team where quality must scale at the same speed as development.

About the Role

We are looking for an AI-Native Software Engineer focused on QA Automation.

This is not a manual testing role.

This is not a role for someone who simply clicks through test cases.

This is a software engineering role for someone who builds systems that prove whether the product works.

Our developers build with AI agents. That means code can arrive faster than traditional QA can review it by hand. We need a QA automation engineer who can build the testing harness, automated checks, synthetic data, test environments, and agent-driven QA workflows that keep up with agent-speed development .

Here is the test: can you build a test system, seed a real bug, prove the test catches it, and explain what is still unverified?

We are not looking for someone who signs off work because it “looks fine.” We are looking for someone who demands evidence.

Our AI-Native QA Vision

QA at Zenara is not a downstream gate. It is an engineering platform.

We are building toward a system where product requirements can become test plans, test data, automated browser and API checks, evidence reports, failure routing, and release confidence — with agents helping create and run the work, and engineers governing the quality.

You will help design and operate this QA system, including:

• Browser and API test runners
• Synthetic test data
• Isolated test environments
• Requirement-to-test workflows
• Automated checks on every change
• Agent-driven QA workflows
• Evidence generation for releases
• Flaky-test reduction
• Failure routing and debugging
• Quality metrics and release confidence

The goal is not to automate bad testing. The goal is to build a system that catches real defects before customers do.

What You’ll Do

• Build and maintain the QA automation harness across browser, API, data, and workflow layers.
• Create automated checks that run on every meaningful change.
• Build synthetic test data and isolated test environments.
• Own the UAT/QA automation pipeline from requirement to test plan to run to evidence.
• Use AI agents to help generate, execute, improve, and debug tests.
• Build independent agent-driven QA workflows that test the work of code-writing agents.
• Prove tests work by reintroducing known bugs and showing they fail correctly.
• Reduce flaky tests and distinguish real product failures from environment noise.
• Produce reliable QA evidence for releases.
• Work with engineering and product to define quality gates for critical user journeys.

What We’re Looking For

• Strong software engineering ability: you can design, write, debug, and maintain code.
• Experience with test automation using Playwright, Cypress, API testing frameworks, or similar tools.
• CI/CD experience and comfort wiring tests into build and release workflows.
• Practical experience using AI agents to create or improve tests.
• Strong judgment about whether a test actually proves anything.
• Ability to seed a bug, run a test, and demonstrate that the test catches the failure.
• Comfort debugging flaky tests, environment issues, and test data problems.
• Skeptical, evidence-driven thinking.
• Clear communication around risk, coverage, and release confidence.

Our Current Stack

• TypeScript
• Node.js, Hono
• React
• Prisma, PostgreSQL
• Redis, BullMQ
• Playwright
• Azure AKS, Blob Storage, Azure Monitor / OpenTelemetry
• Terraform, Helm, CI pipelines
• Medplum / FHIR
• Flowable
• Claude Code, Codex
• Dify-based AI workflows

What “AI-Native QA” Means for This Role

For this role, being AI-native means you can:

• Use agents to generate test ideas, test code, and edge cases.
• Review agent-created tests critically.
• Build workflows where agents test independently from the agents that wrote the code.
• Use AI to analyze failures and propose debugging paths.
• Create repeatable quality gates that scale with development speed.
• Measure whether AI-generated tests are actually useful.
• Avoid false confidence from tests that pass without proving behavior.

The point is not to have more tests. The point is to have trustworthy evidence.

Nice to Have

• Healthcare or regulated-software experience.
• Experience testing AI-powered features, including output quality.
• Experience building test environments and synthetic data.
• Performance or load testing experience.

What Success Looks Like

In your first 30 days, you will map the current QA setup, agree the highest-risk user journeys, and deliver one automated end-to-end QA lane that runs on every change and catches a known bug .

Within your first 90 days, key journeys for Assess and Zenflow will be covered automatically, every release will have QA evidence, and flaky-test rate and run time will be measured and improving .

Over time, success means building a mature agentic QA platform where escaped defects are low and QA throughput keeps pace with agent-speed development .

Outside required collaboration, we care about output, reliability, and quality.

Compensation and Benefits

• Competitive compensation, based on demonstrated ability and experience
• Fully remote work
• Equipment allowance
• Direct access to the founder and Head of Engineering
• The opportunity to build the QA function as an engineering platform from the ground up

Who Will Thrive in This Role

This role is well suited to someone who:

• Thinks like an engineer, not a manual checklist executor.
• Enjoys building tools, systems, and automation.
• Is naturally skeptical and evidence-driven.
• Cares about proving behavior, not just producing green checkmarks.
• Wants to make QA scale with AI-native development.
• Likes working in fast-moving, ambiguous startup environments.

You may struggle if you only have manual testing experience, cannot code, sign off work without evidence, or write tests that pass regardless of whether the product is correct .

If you want to build the quality system for an AI-native software team, we would love to hear from you.