Staff/Principal Solutions Architect (Applied AI + SaaS)

About the role

Following our successful expansion into the U.S. and continued growth across Europe, we are seeking a Staff/Principal Solutions Architect to help drive our Engineering function from our Austin / Barcelona hub. Reporting directly to our Head of Engineering, this person will play a critical role in shaping the architecture of our multi-tenant SaaS platform and the applied-AI layer that sets it apart, making sure both scale safely in a regulated, audited environment.

You will be our most senior architecture voice across the product engineering vertical: the person who turns ambitious ideas into systems that are reliable, secure, and ready for a hard compliance gate.

Key responsibilities

• Own architecture across the product domains: service decomposition, contracts, and clear boundaries.
• Architect the applied-AI layer, agent orchestration, retrieval-augmented generation, and tool-use safety.
• Establish LLM evaluation, observability, and cost-modelling practices that hold up in production.
• Set guardrails for multi-tenancy, data residency, and immutable audit that future infra and data hires build on.
• Write architecture decision records and standards that peers want to follow.
• Partner with platform, research, and scientific stakeholders to ship fast without compromising compliance.

Must-haves

• 10+ years building software, with 4+ as a named architect or tech lead on systems at meaningful scale.
• Strong SaaS platform fundamentals: multi-tenant authorization and entitlements, API gateway and backend-for-frontend patterns, event-driven design, and a database-per-service mindset.
• Security reasoning for cloud-native systems, including the OWASP Top 10 for SaaS and LLM Applications and agent permission scoping.
• Delivery experience in a regulated or audited environment (health, fintech, or government): audit trails, data integrity, and designing for a hard compliance gate.
• Still hands-on: you prototype the risky parts yourself rather than only producing diagrams.

Nice-to-haves

• Recent, hands-on applied-AI work shipped to production: RAG and vector databases, and agentic or multi-agent orchestration (e.g. LangGraph, Semantic Kernel, CrewAI, or equivalent).
• LLM evaluation, observability, and per-conversation cost modelling.
• Life sciences or clinical trials background (FDA 21 CFR Part 11, GxP, eTMF).
• Working fluency with Kubernetes and data-residency design.
• Experience with knowledge graphs and semantic/embedding-based retrieval at scale.
• Track record influencing across organizational boundaries without formal authority.