Job
In this role, you will develop integrations between Dynatrace and leading hyperscalers as well as AI IDE applications while building MCP servers to provide observability data for agentic systems.
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Job technologies
Your role in the 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 to ship integrations for a new hyperscaler or IDE vendor service requires rapid 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 that matter to partner engineers.
- Mentor other engineers and raise the bar on how the team works across org boundaries.
Our expectations of you
Qualifications
- Strong proficiency in at least one of Java, Go, Python, or TypeScript; able to ramp up on a second within a quarter.
- Designed and shipped public-facing or partner-facing APIs (REST and/or gRPC), including versioning, backward 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, and prompt primitives, authentication, and the realities of running MCP in production.
- Built at least one IDE extension, plugin, or integration that real developers have 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 are reranked, and why a well-designed tool schema matters more than a clever prompt.
- Can reason about the trade-offs between agent-facing and human-facing API design (token cost, determinism, error surfacing, idempotency).
Experience
- 5+ years of professional software engineering experience building and operating production backend systems.
- Hands-on, production-grade experience with AWS, Azure, or GCP—building with native SDKs, not just deploying to VMs.
- Practical experience with the Model Context Protocol (MCP) — building MCP servers, clients, or both.
What we offer
- Due to legal requirements, we are required to state a salary range for this position: €90,000–€112,000 gross per year for full-time employment (38.5 hours/week).
Benefits
Topics you will work on
Job locations
About your employer
Barcelona, Detroit, Klagenfurt, Graz, Innsbruck, München, Wien, Linz, Hagenberg, Gdańsk
As passionate and open-minded software enthusiasts, we are the global market leader in software intelligence. With AI-driven, answer-based automation, we support the largest companies in the world to create perfect digital solutions. Our mission is to make sure that millions of people have excellent user experiences in the age of digitalization – whether banking, traveling, shopping or working on the go. By combining observability with automation and AI into one cloud-based platform, we're making this possible.
- 250+ Employees
- 2005
- German, English
- Established Company
- Hybrid, Onsite
- Internet, IT, Telecommunication
Employer reviews
- Engineering
- 4.3
- Working conditions
- 4.9
- Career growth
- 4.6
- Team culture
- 4.5
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