QA Analyst - AI Solutions (Greek Speaking)

Sword Health is shifting healthcare from human-first to AI-first through its AI Care platform, making world-class healthcare available anytime, anywhere, while significantly reducing costs for payers, self-insured employers, national health systems, and other healthcare organizations. Sword began by reinventing pain care with AI at its core, and has since expanded into women’s health, movement health, and more recently mental health. Since 2020, more than 700,000 members across three continents have completed 10 million AI sessions, helping Sword's 1,000+ enterprise clients avoid over $1 billion in unnecessary healthcare costs. Backed by 42 clinical studies and over 44 patents, Sword Health has raised more than $500 million from leading investors, including Khosla Ventures, General Catalyst, Transformation Capital, and Founders Fund. Learn more at www.swordhealth.com. About Sword Intelligence: At Sword Health, we spent years perfecting AI systems across our own patient population of millions, learning what works in real healthcare environments. Now, through Sword Intelligence, we're taking those battle-tested technologies and productizing them for health systems, governments, and insurance companies worldwide. See our website for more information. About the role: Hospitals are hemorrhaging staff, insurance claims are drowning in bureaucracy, and governments can't keep up with demand. While everyone else talks about "digital transformation," we're actually doing it – deploying human + AI solutions that work in the real world, not just in demos. As our QA Analyst, you'll be the guardian of quality for AI Agents that are already changing healthcare. You'll own the complete quality lifecycle, from pre-launch validation to continuous post-deployment improvement. This means you don't just catch problems; you solve them, monitor for new ones, and proactively strengthen our AI Agents. Think of it as being a quality engineer for the future. You'll identify issues, implement solutions, measure the impact, and continuously evolve our AI to perform better. When you spot a problem with how our AI handles a medical scenario, you'll trace it to the root cause, modify the underlying prompt/logic, validate the fix works across different contexts, and establish monitoring to prevent similar issues. This is quality assurance that actually improves the product, not just documents what's wrong with it.