Hey, glad you're here 👋

We know job hunting can feel a little overwhelming, endless tabs, endless job descriptions, and trying to figure out whether a company is actually the right fit (not just whether you fit the role).

So we’ll keep this simple. Here's a look at the role, the team, and the impact you'll have at Hupo. If what you read gets you excited, we'd love to meet you!

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.