Principal AI Full-Stack Engineer

Peak3 (formerly ZA Tech); Madrid; Presencial; EUR 90.000 - 130.000; Jornada completa

Descripción de la vacante

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

You should be comfortable debugging production RAG recalls and collaborating with actuarial and underwriting teams to create explainable, auditable AI systems. The role spans frontend, backend, and cloud deployments.

Formación

• Experience shipping production software at scale, including LLM/AI systems.
• Proficient in Python or TypeScript and a modern frontend framework.
• Hands-on LLM engineering depth: RAG/retrieval, function calling, agent frameworks.
• Solid AI infra grounding: vector databases, embedding models, model serving.
• Strong product instincts and ability to translate ambiguous problems into scoped solutions.
• Experience taking an LLM system from prototype to production with real users and operational load.

Responsabilidades

• Build agentic AI applications end-to-end — RAG pipelines, multi-agent orchestration, tool calling, workflows.
• Design and optimize LLM inference chains: prompts, outputs, fine-tuning, evals, observability.
• Stand up production-grade AI infrastructure: vector DBs, 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 teams to build trustworthy, auditable AI systems.

Conocimientos

Python

TypeScript

Frontend framework

LLM engineering

AI infra

Kubernetes

Problem solving

Open-source

Herramientas

LangGraph

LlamaIndex

Vector databases

Model deployment

Descripción del empleo

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.
• 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.