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# AI Platform Engineer

- **Company:** Peak3
- **Location:** Madrid, Spain
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Cloud Engineering, Python (Programming Language), PostgreSQL, Next.js, Secure Coding, TypeScript, Datadog, Data Logging, ReactJS, Large Language Models, Multi-Agent Systems, Caching, Kubernetes, Machine Learning Operations, Serverless Computing
- **Published:** August 8, 2026
- **Apply:** https://www.jobleads.com/es/job/ee5f24a93e73328fb72ea7037e3eb317f

## About the Role

* 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., * 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.

## Description

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., * 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.


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