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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Principal Software Engineer - Hyperscaler & Strategic Partner Integrations (AI) - **Company:** Dynatrace GmbH - **Location:** Wien, Austria - **Experience:** Expert - **Salary:** €90,000.0 - €112,000.0 - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Cloud Computing, Cursor, Software Debugging, Software Design Documents, Github, Python (Programming Language), OpenAI, Software Engineering, Systems Integration, TypeScript, Management of Software Versions, Visual Studio Online, JetBrains, Google Cloud, GitHub Copilot, Large Language Models, Claude Code, AgentCore, Backend, Agentic-AI, Build Management, AI Platforms, Claude, Google Gemini, Cloudwatch, Model Context Protocol, Marketplace, Dynatrace, Golang - **Published:** October 22, 2026 - **Apply:** https://dejobs.org/x/x/870EB52B1EBD47DAAFDDABCC2461E675/job/ ## About the Role * 5+ years of professional software engineering experience building and operating production backend systems. * 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. Hyperscaler depth (at least one) * Hands-on, production-grade experience with AWS, Azure, or GCP - building against native SDKs, not just deploying to VMs. * 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. Observability and AI workloads * 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. ## Description Dynatrace's growth is increasingly tied to deep, co-engineered partnerships with the three hyperscalers and with the IDE and AI-tooling vendors developers actually use day-to-day. 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. What you will do * 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.