Senior Quality Assurance Engineer

**About Hupo** We are an AI-native start-up building sales enablement products in the banking, financial services, and insurance industry; already trusted by dozens of enterprise customers (including Fortune 500 companies). **Role at a Glance** * **Title:** QA Automation Engineer - AI * **Team:** Engineering * **Employment Type:** Full-time * **Location:** Remote (APAC) **Why This Role Matters** At Hupo, AI quality is the product - a wrong score, hallucinated feedback, or a broken voice turn is a customer-facing defect, and in a regulated BFSI context, a compliance risk. This role owns and scales the testing of Hupo’s AI pipelines, working closely with QA, Engineering, and Product as the platform grows. It’s a hands-on, high-ownership role in a fast-moving environment - you’ll be a key part of deciding, with evidence, whether each release is safe to ship. **What You'll Do** * Build and maintain automated testing for AI voice and chat agents, from single conversational turns to full roleplay flows. * Test LLM output quality - correctness, consistency, structured output, language fidelity, prompt regression - using evaluation harnesses (LLM-as-judge, golden datasets, tolerance-based assertions for non-determinism). * Validate RAG pipelines: retrieval relevance, grounding/faithfulness, and answer quality. * Test voice pipelines: STT/TTS accuracy and real-time, low-latency behavior across languages. * Automate across UI/E2E, API, and AI output layers, and build CI/CD from scratch - lint, type-check, tests, evals, coverage gates, deployment checks. * Own release quality: regression strategy, catching breakages early, and clear go/no-go calls. * Cover database and load/performance testing; extend automation to desktop and mobile clients. * Take over and extend existing QA automation, and strengthen shared frameworks, tooling, and QA processes/standards. **What Success Looks Like Within your first few months, you'll:** * Ramp on Hupo’s existing QA automation suites and take clear ownership of them. * Extend test coverage for LLM and voice-agent behavior, including new evaluation harnesses for non-deterministic output. * Contribute CI/CD improvements - coverage gates, deployment checks - that catch regressions before they reach customers. **You’ll also be expected to continuously:** * Adapt and expand impact as priorities shift * Take ownership of new problems as they emerge **What We're Looking For Must-Haves** * 5+ years as a QA Automation Engineer, with a proven track record testing AI systems - not just traditional software. * Builder mindset: established testing frameworks, standards, and CI/CD-integrated automation from scratch. * Hands-on experience testing conversational AI - voice and/or chat agents - on real, shipped projects. * Strong grasp of LLMs, prompt engineering, and RAG - designing tests for nondeterministic output and evaluating retrieval/generation quality. * Understanding of voice pipelines (speech-to-text, text-to-speech) and how to test them in automation. * Hands-on with LLM evaluation/observability tooling - e.g. Langfuse, LangSmith, DeepEval, RAGAS. * Proficient in UI/E2E (e.g. Playwright) and API test automation; experience building CI/CD pipelines from scratch. * Comfort with database testing, load/performance testing, and desktop/mobile test automation. * Solid Git discipline, basic cloud knowledge, and familiarity with the agile sprint lifecycle. * Track record in fast-paced, fast-shipping environments - ramps quickly on unfamiliar systems, pragmatic about process. **Nice-to-Haves** * TypeScript - for reading the codebase and contributing test/automation code directly. * Voice & real-time: LiveKit / WebRTC; STT/TTS via Azure Speech, Google Cloud Speech, Deepgram, or self-hosted Whisper. * AI/ML infra & eval tooling: GPU inference (vLLM / TGI / Triton), Azure OpenAI, golden datasets, HITL pipelines, Braintrust, Promptfoo. * QA & cloud tooling: Cypress, Postman, multi-cloud (AWS/Azure/GCP), Infrastructure-asCode (Terraform/Pulumi/CloudFormation), Docker/Kubernetes. * Also useful: product analytics familiarity (e.g. PostHog), multilingual/localized testing experience, and production monitoring/error-tracking tools.