Principal Ai Full-Stack Engineer

Peak3; Madrid; Presencial; EUR 90.000 - 130.000; Jornada completa

Descripción de la vacante

Peak3 in Madrid is seeking a Principal AI Full-Stack Engineer to own the design, build, and operation of agentic AI systems powering our core insurance products. You will architect LLM inference chains, build production AI infrastructure, and ship full-stack features from model to UI.

You should thrive in a fast-paced, collaborative environment, have hands-on depth across backend, frontend, and ML tooling, and enjoy turning difficult problems into scalable, auditable solutions for insurers and

Formación

• Proven production-ready LLM/AI system experience.
• Fluent in Python or TypeScript with full-stack capability.
• Hands-on LLM engineering depth: RAG, function/tool calling, agent frameworks.

Responsabilidades

• Build agentic AI applications end-to-end — RAG pipelines, multi-agent orchestration, tool calling, workflows.
• Design and optimize LLM inference chains: prompts, outputs, evals, and observability.
• Stand up production AI infrastructure: vector databases, semantic retrieval, model gateways, inference optimization.
• Own full-stack delivery — frontend (React/Next.js), backend (Python/TypeScript), cloud-native deployment (K8s/Serverless).
• Partner with actuarial, underwriting, and claims to build trustworthy, auditable AI systems.

Conocimientos

LLM Engineering

Python

TypeScript

Frontend Development

Herramientas

LangGraph

LlamaIndex

Kubernetes

Postgres

Descripción del empleo

Principal AI Full-Stack Engineer Peak3 Madrid, Provincia de Madrid, Spain

Descripción del trabajo

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.

Lea el resumen de esta oportunidad para comprender qué habilidades, incluidas las habilidades interpersonales relevantes y el dominio de paquetes de software, se requieren.

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. xqysrnh Prior experience with every part isn't required — strong fundamentals and fast learning matter more.