Descripción de la oferta
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.
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.
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