AI Platform Engineer

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 an AI Platform Engineer on the AI Platform team, you'll build and operate the infrastructure that keeps our agentic AI systems fast, reliable, and cost-effective in production. Working closely with our Principal AI Full-Stack Engineer, you'll take architectural direction and turn it into solid, well-monitored infrastructure — vector databases, model gateways, inference pipelines — that the rest of the team builds on.

Responsibilities

• Build and maintain core AI infrastructure: vector databases, semantic retrieval, model gateways, and inference pipelines.
• Implement inference optimization and caching to keep latency and cost within target.
• Set up and maintain observability for LLM systems — logging, tracing, evals monitoring, cost tracking.
• Support production incidents and on-call for AI systems, including runbooks and postmortems.
• Work with the Principal AI Full-Stack Engineer to implement architecture decisions and contribute to full-stack features when needed.

Experience & Qualifications

• Solid experience in Python or TypeScript, with working knowledge of backend infrastructure and cloud-native deployment (K8s or serverless).
• Hands-on experience with vector databases, embedding models, and model deployment/serving patterns.
• Some experience with LLM inference optimization, caching, and observability tooling.
• Familiarity with RAG pipelines, function/tool calling, or agent frameworks (LangGraph, LlamaIndex, or similar) — deep expertise not required, willingness to grow into it is.
• Comfortable operating in production environments and taking ownership of reliability and performance.
• High agency and a builder mindset — willing to dig into infra problems without waiting for complete specs.

Nice to Have

• Experience with secure code execution sandboxes (gVisor, Firecracker, WASM) or long-running workflow orchestration.
• Exposure to model routing across providers or agent memory/state architectures.
• Finance or insurance domain experience (not required — engineering fundamentals 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.

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