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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Principal Software Engineer - Applied AI & Agentic Systems - **Company:** Matrix42 AG - **Location:** Frankfurt am Main, Germany (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Automation of Tests, Microsoft Azure, C Sharp (Programming Language), Software as a Service, Cloud Engineering, Continuous Integration, Programming Tools, Fault Tolerance, Python (Programming Language), Knowledge Management, Metadata, Open Source Technology, Software Engineering, Data Streaming, TypeScript, Enterprise Software Applications, GitHub Copilot, Large Language Models, Multi-Agent Systems, Model Validation, AI Platforms, Information Technology, Production Code, Enterprise Integration, Data Management, Api Design, Matrix42, Data Pipelines - **Published:** August 15, 2026 - **Apply:** https://www.adzuna.de/details/5842030324 ## About the Role * 7+ years of professional software engineering experience, or equivalent evidence of senior/principal-level impact in production product development. * A strong record of shipping and operating customer-facing software-not only notebooks, proofs of concept, demos, or advisory work. * Professional proficiency in Python and strong ability in at least one product engineering language such as C#, TypeScript, or Java. * Hands-on experience building production LLM or agentic applications, including several of the following: retrieval and grounding, embeddings or hybrid search, structured outputs, tool or function calling, context and state management, model selection or routing, and human approval or escalation. * Experience with data-intensive systems: APIs and integrations, event or streaming data, ingestion and transformation, data quality, metadata, storage, and operational observability. * Strong system-design fundamentals for API-first, distributed, cloud-native, multi-tenant SaaS products, including identity, authorization, reliability, performance, and secure integration with enterprise systems. * Practical experience evaluating and diagnosing AI systems through datasets, traces, qualitative review, quantitative metrics, automated tests, CI/CD, and production telemetry. * Pragmatic architectural judgment. You know when conventional software is sufficient, when AI adds real value, and how to balance fast validation with a credible path to production. * Clear communication and product thinking. You can work directly with developers and customers, translate ambiguous needs into a testable technical plan, explain trade-offs, and drive an end-to-end result across team boundaries. * A degree in computer science, software engineering, AI/ML, or a related field-or equivalent practical experience. Nice to have * Experience with observability, telemetry, operational intelligence, or other high-volume event-data platforms. * Knowledge of IT Service Management, Enterprise Service Management, endpoint management, knowledge management, service automation, or employee self-service. * Experience with Microsoft Azure, Azure OpenAI/Microsoft Foundry, Semantic Kernel, or comparable enterprise AI platforms. * Practical knowledge of MCP or comparable tool-integration protocols, agent orchestration frameworks, vector or hybrid search, and OpenTelemetry-based tracing. * Experience with hosted and open-source models, provider-agnostic or bring-your-own-model architectures, and hybrid or on-premises deployments. * Experience evolving a substantial installed enterprise product base toward a new architecture-not only building greenfield products. * Familiarity with European data-sovereignty, privacy, security, and responsible-AI requirements. * Experience mentoring engineers, creating reusable technical patterns, and using agentic development tools such as GitHub Copilot, Claude Code, Gemini CLI, or OpenAI Codex. ## Description * Own selected AI capabilities end to end-from problem discovery and technical design through implementation, evaluation, release, production monitoring, and continuous improvement. * Write and review production code across AI services, AI harnesses, APIs, connectors, background services, MCP-compatible tools, data pipelines, and the product surfaces needed to deliver a complete workflow. * Design agentic systems using the simplest architecture that works, including retrieval and grounding, structured outputs, tool execution, state and context, model routing, approval flows, fallbacks, and graceful degradation. * Translate each use case into concrete data and platform requirements. Work with APIs, event streams, ingestion and transformation, data quality and alignment, metadata, storage, observability, and governance so that AI results are trustworthy. * Create results early: build focused end-to-end prototypes with small teams, validate them against real workflows, and evolve successful patterns into secure, maintainable, multi-tenant product capabilities. * Make evaluation part of engineering. Build representative datasets, automated and human-reviewed evaluations, trace analysis, regression gates, and telemetry for quality, task completion, latency, safety, and cost. * Engineer for enterprise trust through tenant isolation, least-privilege tool access, identity and authorization, auditability, data minimization, prompt-injection defenses, human approval for consequential actions, feature flags, and safe rollback. * Collaborate closely with Product, Design, Architecture, Security, Support, Customer Success, and engineering teams. Turn customer problems into measurable outcomes, unblock developers, contribute reusable libraries and reference implementations, and raise applied-AI engineering practices across Matrix42. ## Related Videos - [API Design - Getting Started](https://www.wearedevelopers.com/videos/33-api-design-getting-started) - [A Data Mesh needs Open Metadata](https://www.wearedevelopers.com/videos/505-a-data-mesh-needs-open-metadata) - [Agentic employees in world's most downloaded FinTech app](https://www.wearedevelopers.com/videos/100123-agentic-employees-in-world-s-most-downloaded-fintech-app) - [Rest API Antipatterns](https://www.wearedevelopers.com/videos/100208-rest-api-antipatterns) - [Crafting Custom Frameworks with Rust: A Deep Dive into Procedural Macros](https://www.wearedevelopers.com/videos/849-crafting-custom-frameworks-with-rust-a-deep-dive-into-procedural-macros) - [The AI-Native Engineering Org: What’s Real, What’s Hype, What’s Next](https://www.wearedevelopers.com/videos/100004-the-ai-native-engineering-org-what-s-real-what-s-hype-what-s-next) ## Related Articles - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [What is Agentic Programming and Why Should Developers Care?](https://www.wearedevelopers.com/magazine/625-what-is-agentic-programming-and-why-should-developers-care) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [What is Software Engineering in the Age of AI?](https://www.wearedevelopers.com/magazine/640-what-is-software-engineering-in-the-age-of-ai) - [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline)