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# Senior Platform Engineer (Agentic Experience)

- **Company:** Moss GmbH
- **Location:** München, Germany
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, JIRA, Cloud Engineering, Continuous Integration, Github, Data Logging, Data Classification
- **Published:** June 21, 2026
- **Apply:** https://de.indeed.com/viewjob?jk=bd8541e722ac143d

## About the Role

Do you have experience in Jira?, * 8+ years of experience in cloud-native startups, FinTechs, or high-volume engineering environments

 * 3+ years working in a team focused on developer experience
 * 1-2 years using modern agentic tools in a professional setting
 * 5+ years building integrations between services
 * Business fluent in English (C1 or above)

In addition, here are the skills and attributes we are looking for:

 * Comfortable navigating ambiguity and making decisions as new information emerges, without waiting for the problem to be fully defined
 * Able to identify what needs solving, not just execute what has been scoped
 * Brings rigour to fast-moving tooling decisions, weighing productivity gains against security and compliance constraints in a regulated environment
 * Builds trust with engineering teams through hands-on presence, not top-down mandates

## Description

You will report to the Director of Engineering and sit within a Platform Engineering team of around eight people, working alongside Platform, Data, and AI Engineers. The problems here are rarely well-defined upfront - this role is for someone who identifies what needs solving, not someone who waits for a Jira ticket., * Build a developer productivity measurement platform integrating GitHub, Linear, Slack, and CI/CD signals into a coherent view of delivery flow

 * Identify and remove friction from engineering workflows: slow pipelines, poor feedback loops, unclear ownership, and noisy alerting
 * Define, deploy, and iterate on AI tooling for local development, including IDE integrations, agentic tools, MCP servers, and model integrations such as Claude and Codex
 * Build the guardrails that make AI adoption safe: prompt logging, data classification boundaries, an approved model catalogue, and usage observability
 * Understand the workflows of engineering and product teams to surface bottlenecks, systemic issues, and drive improvements with their buy-in.
 * Write documentation that engineers actually use: runbooks, adoption guides, and decision records


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