{"id":2926618,"url":"https://alion.io/job/zenara-health-ai-native-software-engineer-qa-automation","title":"AI-Native Software Engineer, QA Automation","company":{"id":689103,"name":"Zenara Health","domain":"zenarahealth.com","url":"https://alion.io/company/zenarahealth","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":null},"role":"QA","role_family":"QA","seniority":null,"employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"posting_text","remote_working_hours":{"label":"PT","utc_offset_min":-8,"utc_offset_max":-8},"hiring_geo_confidence":"structured","locations":[],"countries":[],"hiring_countries":["IN"],"hiring_countries_total":1,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"CI/CD","optional":false},{"name":"Cypress","optional":false},{"name":"Playwright","optional":false},{"name":"Synthetic Data","optional":false},{"name":"Azure","optional":true},{"name":"Azure AKS","optional":true},{"name":"BullMQ","optional":true},{"name":"Claude Code","optional":true},{"name":"Dify","optional":true},{"name":"FHIR","optional":true},{"name":"JavaScript","optional":true},{"name":"Kubernetes","optional":true},{"name":"Node JS","optional":true},{"name":"OpenAI Codex","optional":true},{"name":"OpenTelemetry","optional":true},{"name":"PostgreSQL","optional":true},{"name":"Prisma","optional":true},{"name":"React.js","optional":true},{"name":"Redis","optional":true},{"name":"Terraform","optional":true},{"name":"TypeScript","optional":true}],"status":"live","first_seen_at":"2026-10-20T13:40:37Z","employer_posted_date":"2026-10-20","last_verified_at":"2026-11-02T08:04:51Z","board_verified":true,"closed_at":null,"days_open":12,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":12},"description":"AI-Native Software Engineer, QA Automation - Mid to Senior | Zenara Health\nLocation: Remote - India\nType: Full-time\nWorking 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.\nCommitment: Approximately 50 hours per week\nIndustry: Healthcare technology experience is preferred but not mandatory\nAbout the Company\nZenara 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 .\nWe are a startup, not a department. We are building an AI-native engineering team where quality must scale at the same speed as development.\nAbout the Role\nWe are looking for an AI-Native Software Engineer focused on QA Automation.\nThis is not a manual testing role.\nThis is not a role for someone who simply clicks through test cases.\nThis is a software engineering role for someone who builds systems that prove whether the product works.\nOur 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 .\nHere is the test: can you build a test system, seed a real bug, prove the test catches it, and explain what is still unverified?\nWe are not looking for someone who signs off work because it “looks fine.” We are looking for someone who demands evidence.\nOur AI-Native QA Vision\nQA at Zenara is not a downstream gate. It is an engineering platform.\nWe 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.\nYou will help design and operate this QA system, including:\nBrowser and API test runners\nSynthetic test data\nIsolated test environments\nRequirement-to-test workflows\nAutomated checks on every change\nAgent-driven QA workflows\nEvidence generation for releases\nFlaky-test reduction\nFailure routing and debugging\nQuality metrics and release confidence\nThe goal is not to automate bad testing. The goal is to build a system that catches real defects before customers do.\nWhat You’ll Do\n Build and maintain the QA automation harness across browser, API, data, and workflow layers. \n Create automated checks that run on every meaningful change. \n Build synthetic test data and isolated test environments. \n Own the UAT/QA automation pipeline from requirement to test plan to run to evidence.\n Use AI agents to help generate, execute, improve, and debug tests. \n Build independent agent-driven QA workflows that test the work of code-writing agents. \n Prove tests work by reintroducing known bugs and showing they fail correctly. \n Reduce flaky tests and distinguish real product failures from environment noise. \n Produce reliable QA evidence for releases. \n Work with engineering and product to define quality gates for critical user journeys. \nWhat We’re Looking For\n Strong software engineering ability: you can design, write, debug, and maintain code. \n Experience with test automation using Playwright, Cypress, API testing frameworks, or similar tools. \n CI/CD experience and comfort wiring tests into build and release workflows. \n Practical experience using AI agents to create or improve tests. \n Strong judgment about whether a test actually proves anything. \n Ability to seed a bug, run a test, and demonstrate that the test catches the failure. \n Comfort debugging flaky tests, environment issues, and test data problems. \n Skeptical, evidence-driven thinking. \n Clear communication around risk, coverage, and release confidence. \nOur Current Stack\n TypeScript \n Node.js, Hono \n React \n Prisma, PostgreSQL \n Redis, BullMQ \n Playwright \n Azure AKS, Blob Storage, Azure Monitor / OpenTelemetry \n Terraform, Helm, CI pipelines \n Medplum / FHIR \n Flowable \n Claude Code, Codex \n Dify-based AI workflows \nWhat “AI-Native QA” Means for This Role\nFor this role, being AI-native means you can:\nUse agents to generate test ideas, test code, and edge cases.\nReview agent-created tests critically.\nBuild workflows where agents test independently from the agents that wrote the code.\nUse AI to analyze failures and propose debugging paths.\nCreate repeatable quality gates that scale with development speed.\nMeasure whether AI-generated tests are actually useful.\nAvoid false confidence from tests that pass without proving behavior.\nThe point is not to have more tests. The point is to have trustworthy evidence.\nNice to Have\n Healthcare or regulated-software experience. \n Experience testing AI-powered features, including output quality. \n Experience building test environments and synthetic data. \n Performance or load testing experience. \nWhat Success Looks Like\nIn 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 .\nWithin 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 .\nOver time, success means building a mature agentic QA platform where escaped defects are low and QA throughput keeps pace with agent-speed development .\nOutside required collaboration, we care about output, reliability, and quality.\nCompensation and Benefits\nCompetitive compensation, based on demonstrated ability and experience\nFully remote work\nEquipment allowance\nDirect access to the founder and Head of Engineering\nThe opportunity to build the QA function as an engineering platform from the ground up\nWho Will Thrive in This Role\nThis role is well suited to someone who:\nThinks like an engineer, not a manual checklist executor.\nEnjoys building tools, systems, and automation.\nIs naturally skeptical and evidence-driven.\nCares about proving behavior, not just producing green checkmarks.\nWants to make QA scale with AI-native development.\nLikes working in fast-moving, ambiguous startup environments.\nYou 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 .\nIf you want to build the quality system for an AI-native software team, we would love to hear from 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