Senior Full Stack Engineer (f/m/d)
Kyanhealth
| Company | Kyanhealth |
| Category | Engineering |
| Location | REMOTE |
| Remote | Remote |
| Employment | Not stated |
| Level | Senior |
| Salary | Not stated by the employer |
| Posted | 18 May 2026 |
| Last verified | 4 Aug 2026 |
| Source | Employer career page (ashby) |
Description
ABOUT US:
We are one of the fastest-growing advanced Employee Assistance Program (EAP) and mental wellbeing platforms in the world. Founded in Switzerland in 2021, we support more than 3 million covered lives across 90+ countries, trusted by market-leading multinational enterprises and backed at Series A by Swisscom Ventures, Amplo, and GreyMatter Capital.
Our team of 35 individuals of 15+ nationalities is employed directly through our legal entities in Germany, Switzerland, and the US (Texas, Rhode Island, Florida), via Employer of Record (EOR) partners in Canada, Spain, the Netherlands, Belgium, South Africa, Denmark and the UK, and through freelancers in Pakistan and other locations.
SOME THINGS WE’RE PROUD OF
- 🧠 Building an industry leading solution for mental well-being
- 🌎 300.000+ users across 90+ countries
- 🤝 Partnered with 200+ licensed psychologists and coaches
- 💲 €15M+ venture capital raised from renowned investors
- 🎁 Awesome benefits, including unlimited access to our Comprehensive Mental Health App
About The Role
Kyan Health is building the most advanced AI-augmented mental health and wellbeing platform in Europe. Our mission is to bring high-quality, clinically-grounded psychological support to employees and families across multiple countries, languages, and cultural contexts — with AI playing a central role.
We are looking for a Senior Full Stack Engineer with deep AI experience, particularly in:
1. AI agents & multi-step reasoning systems
2. LLM application architectures
3. Tool-calling, retrieval, orchestration, and evaluation
4. Modern AI toolchains and best practices
You will work closely with engineering, data, product, and clinical teams to build our AI features end-to-end and help shape the future of our platform.
This is a role with massive influence — we’re early enough that your architectural choices, code, and AI expertise will have a lasting impact.
Our stack
(You don’t need all of this, but strong React/TypeScript + AI foundations are essential.)
Frontend
- TypeScript
- React + Next.js
- React Native (plus React Native Web)
- Component libraries/design system
Backend & Data
- Node.js (TypeScript)
- Event-driven and service-oriented architecture
- BigQuery, PostgreSQL, NoSQL stores
- Vector databases, retrieval pipelines
- Cloud infra (GCP/Azure)
AI Tooling & Ecosystem
- OpenAI API & Assistants
- Azure OpenAI (GPT, embeddings, structured outputs)
- LangChain, LlamaIndex, OpenAI function calling
- Agent frameworks (custom, or e.g. LangGraph-style)
- Evaluation suites, guardrails, prompt tooling
- Monitoring + model observability
What You'll do:
AI Architecture & Agentic Systems
- Design and implement coding agents, task-specific AI agents, and multi-step workflows that power user-facing features.
- Build and refine retrieval pipelines, embeddings flows, and hybrid RAG/logic systems.
- Architect robust prompting, function-calling, and tool orchestration systems.
Full Stack Product Development
- Own features across Next.js, backend services, and mobile/web.
- Translate product and clinical requirements into AI-powered user experiences.
- Work with data pipelines, BigQuery, analytics, and experimentation frameworks.
Cross-platform UI Systems
- Build and evolve our shared React/React Native component library and design system.
- Ensure accessibility, performance, and multi-market language support.
Technical Leadership
- Set coding standards for AI-integrated features.
- Mentor others on AI architecture, LLM patterns, evaluation, and failures modes.
- Challenge assumptions, propose better approaches, and push for pragmatic, responsible AI usage.
Who We're Looking For
Required:
- Senior-level engineering experience, ideally 6+ years, with strong autonomy.
- Deep understanding of the modern LLM ecosystem: models, embeddings, fine-tuning, structured outputs, evaluation, laten
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