Job
In dieser Position entwickelst du Integrationen zwischen Dynatrace und führenden Hyperscalern sowie AI-IDE-Anwendungen, während du MCP-Server baust, um Observability-Daten für agentische Systeme bereit zu stellen.
Job-Technologien
Deine Rolle im Team
- Recent integrations show that hyperscalers and IDE providers are no longer just deployment targets but co-development partners.
- The Model Context Protocol (MCP) has become the connective tissue: Dynatrace ships MCP servers so that Claude, Cursor, VS Code, JetBrains, Kiro, and other AI-assisted IDEs can query observability data natively.
- At the same time, customer workloads are shifting from traditional services to agentic AI systems that need a fundamentally new kind of observability.
- This role sits at that intersection.
- You will build the product code that makes Dynatrace work seamlessly with hyperscaler AI services and with the AI-native IDEs developers use to build on top of them, in close technical collaboration with engineering counterparts at AWS, Microsoft, Google, Anthropic, GitHub, and Cursor.
- The work is shipping code - APIs, agents, integrations, MCP servers, IDE extensions, telemetry pipelines - with partner engineers in the room.
- Design and build integrations between Dynatrace and hyperscaler AI / observability services.
- Build and maintain Dynatrace's Model Context Protocol (MCP) servers so AI assistants and agents can query problems, traces, logs, and Dynatrace Intelligence insights as first-class tools - and contribute upstream to the MCP spec where it matters.
- Co-develop with IDE providers (Anthropic / Claude Code, GitHub Copilot, JetBrains AI, Cursor, Windsurf, AWS Kiro, Gemini CLI) so Dynatrace observability shows up where developers already work - plugins, extensions, slash commands, MCP integrations.
- Co-develop with partner engineering teams at AWS, Microsoft, Google, and IDE vendors - joint design docs, shared backlogs, paired debugging across org boundaries.
- Extend Dynatrace's OneAgent and OpenTelemetry pipelines to capture traces, metrics, and evaluations from agentic AI workloads (tool calls, prompt chains, model latency, hallucination signals).
- Contribute to launch-partner integrations - being one of the first observability providers shipping against a new hyperscaler or IDE-vendor service requires fast iteration against pre-GA APIs.
- Own a slice of the partner-facing SDK, MCP server, or IDE-extension surface end-to-end: code, tests, docs, marketplace listing, on-call.
- Represent Dynatrace engineering in technical reviews with partners; translate Dynatrace Intelligence and causal-AI capabilities into terms partner engineers care about.
- Mentor other engineers and raise the bar on how the team works across org boundaries.
Unsere Erwartungen an dich
Qualifikationen
- Strong proficiency in at least one of Java, Go, Python, or TypeScript; able to ramp on a second within a quarter.
- Designed and shipped public-facing or partner-facing APIs (REST and / or gRPC), including versioning, backwards compatibility, and SDK ergonomics.
- Concrete familiarity with the partner-relevant surface of that cloud:
- AWS: Bedrock / AgentCore, Lambda, EKS, CloudWatch, AWS Marketplace, Partner Network (APN).
- Azure: Azure OpenAI, AKS, Azure Monitor, SRE Agent, Azure Marketplace, MISA.
- GCP: Vertex AI / Gemini, GKE, Cloud Operations, Google Cloud Marketplace, Partner Advantage.
- Working knowledge of observability primitives: metrics, logs, distributed tracing, OpenTelemetry instrumentation.
- Understanding of how to instrument LLM and agentic-AI workloads: prompt / response capture, token usage, tool-call traces, evaluation signals, latency and cost attribution.
- Comfortable reasoning about SLOs, error budgets, and incident response in customer-facing systems.
- Comfortable with stdio and HTTP transports, tool / resource / prompt primitives, auth, and the realities of running MCP in production.
- Built at least one IDE extension, plugin, or integration that real developers used - for VS Code, JetBrains, Cursor, Claude Code, GitHub Copilot, Gemini CLI, Kiro, or similar.
- Understands the AI-assisted developer workflow end-to-end: how agents discover tools, how context windows fill up, how tool outputs get re-ranked, why a well-designed tool schema matters more than a clever prompt.
- Can reason about trade-offs of agent-facing API design vs. human-facing API design (token cost, determinism, error surfacing, idempotency).
Erfahrung
- 5+ years of professional software engineering experience building and operating production backend systems.
- Hands-on, production-grade experience with AWS, Azure, or GCP - building against native SDKs, not just deploying to VMs.
- Practical experience with the Model Context Protocol (MCP) - building MCP servers, clients, or both.
Unser Angebot
- Due to legal reasons, we are obliged to list a salary range for this position, which is € 90,000 - € 112,000 gross per year based on full-time employment (38.5 h/week).
Benefits
Themen, mit denen du dich im Job beschäftigst
Job-Standorte
Das ist dein Arbeitgeber
Barcelona, Detroit, Klagenfurt, Graz, Innsbruck, München, Wien, Linz, Hagenberg, Gdańsk
Dynatrace lets customers understand their business like never before, so they can see beyond the complexity, find and fix problems faster and automate manual tasks with Al — so they can focus on what truly matters: running their business. In a world that runs on a software, our Al-driven insights cut through the noise, allowing you to focus on what truly matters by automating manual tasks and resolving issues with pinpoint accuracy.
- 250+ Mitarbeitende
- 2005
- Deutsch, Englisch
- Etablierte Firma
- Hybrid, Onsite
- Internet, IT, Telekom
Arbeitgeber-Reviews
- Engineering
- 4,3
- Arbeitsbedingungen
- 4,9
- Karriere
- 4,6
- Teamkultur
- 4,5
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