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# AI Platform Engineer

- **Company:** Allianz Group
- **Location:** Barcelona, Spain (Remote available)
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Application Release Automation, Software as a Service, Continuous Integration, Python (Programming Language), Machine Learning, Microsoft Platform Builder, Site Reliability Engineering Practices, Elearning, Software Engineering, TypeScript, Cloud Platform System, AI Platforms, Kubernetes, Machine Learning Operations, Terraform
- **Published:** September 17, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=ce0606e5d570c1e9

## About the Role

* Proven track record building operating AI or developer platforms into production scale, ideally in regulated or large-scale environments (banking, large SaaS, tech).
 * Hands-on platform builder with an enablement mindset with experience in shipping platform capabilities used by multiple teams.
 * Deep knowledge of cloud-native infrastructure (Kubernetes, networking), MLOps/LLMOps stacks, vector databases/embedding stores, routing/caching layers, and identity/risk management.
 * Strong software engineering skills (e.g., Python/TypeScript/Go), infrastructure as code (Terraform), CI/CD, and SRE practices for reliability, performance, and cost efficiency.
 * Experience implementing governance, compliance, safety guardrails, and AI FinOps (quotas, budgeting, and policy enforcement) at the platform layer.
 * Pragmatic product sense with excellent vendor evaluation and partnership skills; comfortable making decisions under ambiguity and collaborating across functions.
 * Fluency in English
 * Familiarity with Machine learning is a strong advantage.

## Description

* Design, build, and operate the AI platform runtime (Kubernetes-based services, gateways, and routing) including model routing, caching, shared RAG components, and prompt/skills management.
 * Provide a plug-and-play surface for teams with AI FinOps, quotas, usage metering, and safety/compliance guardrails built in.
 * Define and maintain platform standards (frameworks, SDKs, Spec Kits, MCP interfaces), curating the AI tool landscape and managing lifecycles from sandbox to standardization or retirement.
 * Implement platform-wide observability (metrics, logs, traces), SLOs, cost and performance instrumentation, and incident response processes.
 * Partner with AI Gateway, governance owners, hyperscalers, AI frontier labs for regulated industry features, terms of service and roadmap influence.
 * Optimize platform scalability, reliability, and cost across regions and environments; drive capacity planning, release automation, and risk recovery.


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