Job Opportunities API

TeamViewer provides a leading Digital Workplace platform that connects people with technology—enabling, improving and automating digital processes to make work work better. Our software solutions harness the power of AI and shape the future of digitalization. We believe that our diverse teams and strong company culture are key to the success of our products and technologies, that hundreds of millions of users around the world and around 645,000 customers across all industries rely on. With more than 1,900 employees worldwide, we celebrate the unique perspectives and talents that each individual brings to the table and foster a dynamic work environment where new ideas thrive. Are you ready to join our team and make an impact? Responsibilities The Agentic Ecosystem team is building the foundation for AI-native enterprise software. We create platform capabilities that allow AI agents to understand context, discover tools, invoke capabilities, and execute governed actions across TeamViewer products and connected third-party systems. The work sits at the core of agentic experiences: MCP servers and clients, tool catalogs, context assembly, orchestration, evaluation, observability, secure integration patterns, and production-grade guardrails. The goal is to make agent behavior reliable, measurable, auditable, cost-aware, and safe for enterprise use. You will help define how autonomous agents become trusted components of real-world software, not experiments or demos. This means combining strong engineering standards, AI-native development practices, human accountability, and customer trust. Join us if you want to build the ecosystem that will power the next generation of intelligent digital work. Own the architecture of TeamViewer’s agentic platform, including Tia, third-party agents, tool use, retrieval, memory, orchestration, and governed actions across TeamViewer ONE. Define reference architecture for MCP servers and clients, tool catalogues, context assembly, memory, orchestration, and trust boundaries. Establish evaluation and observability discipline by defining quality criteria, tracing agent behavior, catching regressions before release, and using results to guide architectural decisions. Set pragmatic positions on model choice, routing, fallback behavior, cost governance, vendor dependency, and production reliability. Design safety architecture with security partners, covering prompt injection containment, permission models, tenancy isolation, auditability, and AI component risk. Guide teams hands-on through early project phases, conduct architecture and design reviews, mentor senior engineers, and maintain clear architecture documentation for both engineers and agents. Requirements 10+ years of software engineering experience, including demonstrated expertise in complex architectures, strong Python and TypeScript skills, and deep knowledge of distributed systems. Extensive experience designing and delivering agentic systems, including tool-using agents, MCP or comparable protocols, retrieval, context engineering, multi-agent orchestration, and evaluation frameworks. Proven track record of bringing AI solutions into enterprise production environments, with a strong understanding of security, compliance, multi-tenancy, cost management, reliability, and operational excellence at scale. Strong ability to evaluate AI systems responsibly by defining quality standards, developing evaluation approaches, interpreting results, and identifying real-world failure modes. Deep understanding of common AI model failure modes, including hallucinations, context degradation, prompt injection, non-determinism, and silent regressions, as well as the architectural controls required to mitigate them. Regular use of AI coding agents, combined with critical review practices and accountability for correctness, security, maintainability, and architectural alignment. Ability t