Principal AI Full-Stack Engineer - madrid

About Peak3

Peak3 is an award-winning vertical SaaS provider, enabling more relevant, convenient, and affordable insurance protection for everyone through our technology and ingenuity. Together with our clients, we create a more resilient and innovative future.

We combine insurance core, distribution, and AI solutions to deliver a step change in performance for insurers, MGAs, and insurance intermediaries. From greenfield embedded insurance ventures to multi-country core modernization programs, our SaaS solutions power top customers across life, health, and P&C insurance.

Our 500+ colleagues are based across over 15 countries in Europe, Asia and the Middle East – with an ambitious roadmap to scale further.

About The Role

As a Principal AI Full-Stack Engineer on the AI Platform team, you'll own the design, build, and operation of the agentic AI systems powering our core insurance products — architecting LLM inference chains, building production AI infrastructure, shipping full-stack features from model to UI, and setting technical direction for the team. You should be as comfortable debugging a RAG recall failure in production as whiteboarding agent memory architectures with research or debating interaction patterns with design.

Responsibilities

• Build agentic AI applications end-to-end — RAG pipelines, multi-agent orchestration, tool calling, workflows — and ship insurance agents that run in production.
• Design and optimize LLM inference chains: prompt engineering, structured outputs, fine-tuning (LoRA/PEFT), evals, and observability.
• Stand up production-grade AI infrastructure: vector databases, semantic retrieval, model gateways, inference acceleration, and cost governance.
• Own full-stack delivery — frontend (React/Next.js), backend (Python/TypeScript), cloud-native deployment (K8s/Serverless) — taking demos to policy volumes in the millions.
• Partner with actuarial, underwriting, and claims experts to turn domain knowledge into trustworthy, explainable, auditable AI systems.

Experience & Qualifications

• Proven track record shipping and operating production software at scale, ideally including an LLM/AI system taken from prototype to production.
• Fluent in Python or TypeScript and a modern frontend framework, with genuine comfort spanning backend, frontend, infrastructure, data, and model code.
• Hands-on LLM engineering depth: RAG/retrieval, function/tool calling, agent frameworks (LangGraph, LlamaIndex, or custom), systematic evals, prompt- and model-level optimization.
• Solid AI infrastructure grounding — vector databases, embedding models, model deployment/serving, inference optimization, distributed systems — with the operational discipline for regulated environments.
• Strong product instincts: can turn ambiguous problems into well-scoped solutions, cares about real user impact, and argues for simplicity when complexity isn't earned.
• High agency — prototypes and drives progress without waiting for complete specs.

Nice to Have

• Taken an LLM system from 0 to 1 — prototype to real users, revenue, and operational load, including on-call and incident reviews.
• Agent infrastructure experience: secure execution sandboxes (gVisor, Firecracker, WASM), agent memory/state architectures, model routing, workflow orchestration.
• External technical signals — open-source contributions, technical writing, conference talks.
• Interest in multimodal AI, long-context engineering, applied AI safety/alignment, or RLHF.
• Finance or insurance domain experience (engineering ability and learning velocity matter more).

Our stack: Python, TypeScript, React/Next.js, Postgres, Kubernetes, and various vector databases and LLM providers. Prior experience with every part isn't required — strong fundamentals and fast learning matter more.