Ai Platform Engineer

Peak3 (Formerly Za Tech)
Madrid, Spain
8 days ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours

Tech stack

Artificial Intelligence Software as a Service Cloud Engineering Distributed Systems Python (Programming Language) PostgreSQL Next.js Secure Coding TypeScript Data Logging ReactJS Large Language Models
+7 more
Multi-Agent Systems Caching Backend AI Platforms Kubernetes Machine Learning Operations Serverless Computing

Job description

About Peak3Peak3 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 RoleAs an AI Platform Engineer on the AI Platform team, youll 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, youll 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.ResponsibilitiesBuild 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 & QualificationsSolid 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 HaveExperience with secure code execution sandboxes (gVisor, Firecracker, WASM) or long-running workflow orchestration.Exposure to model routing across providers or agent memory/state architectures.Interest in distributed systems, cost governance, or FinOps for AI workloads.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 isnt required - strong fundamentals and fast learning matter more.

Requirements

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 HaveExperience with secure code execution sandboxes (gVisor, Firecracker, WASM) or long-running workflow orchestration.Exposure to model routing across providers or agent memory/state architectures.Interest in distributed systems, cost governance, or FinOps for AI workloads.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 isnt required - strong fundamentals and fast learning matter more.

About the company

About Peak3Peak3 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 RoleAs an AI Platform Engineer on the AI Platform team, youll build and operate the infrastructure that keeps our agentic AI systems fast, reliable, and cost-effective in production.

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Good distractions

Talks and stories from around this role — technically off-topic, practically not.

3:45 min

Fusing developer experience and platform engineering for agentic SDLC

Julia Kordick Julia Kordick · World Congress 2026 Europe

4:30 min

Scaffolding pages and routing single-page applications with Next.js

Josh Goldberg · JS Congress

1:21 min

Exploring the target application for front end tests

Anna Mcdougall · JS Congress

1:52 min

Structuring and scaling the backend engineering team

Stefan Lingler Stefan Lingler +1 · Coffee With Developers

3:03 min

Career evolution in data engineering and AI platforms

Maria Apazoglou · Coffee With Developers

1:31 min

Introduction to the GraphQL, Apollo, and Next.js stack

Josh Goldberg · JS Congress

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