About this role
Engineers who thrive on embedding directly with enterprise customers to build production systems from the ground up will find a natural fit in this Munich-based position. Operating as part of a local Enterprise Sales Pod, the Forward Deployed Engineer owns the complete technical lifecycle from initial discovery and architecture through proof-of-concept and production go-live on Telnyx's global network. Working hand-in-hand with an Enterprise Account Executive, the focus centers on delivering real-world outcomes such as AI contact centers, voice assistants, and enterprise inference rather than simple product demonstrations.
Day-to-day responsibilities involve designing custom implementations, running open-weight LLMs, deploying model gateways, and establishing robust observability practices for enterprise clients across Germany, Austria, and Switzerland. Candidates must bring a strong background in software engineering, cloud-native infrastructure, and customer-facing technical execution. The ideal person is self-sufficient by default, capable of translating vague customer problems into root causes, and fluent in both English and German to conduct whiteboard sessions and executive presentations.
What you will do
- Embed directly with enterprise customers to understand communications workflows, artificial intelligence use cases, and integration challenges.
- Build and deploy custom implementations including artificial intelligence voice assistants, Telnyx APIs, and WebRTC.
- Run open-weight large language models in customer environments using Telnyx Inference or self-hosted stacks when sovereign cloud requirements demand it.
- Deploy and operate LiteLLM as a model gateway in customer environments with routing, load balancing, retries, and fallbacks.
- Wire production observability for everything shipped, utilizing metrics, logs, traces, dashboards, and alerting systems.
- Design model routing strategies for real-time voice workloads while balancing latency, cost, and quality.
- Adapt models to customer domains by building prompt and retrieval-augmented generation pipelines and evaluation harnesses for German-language use cases and regional regulations.
- Create clear technical documentation, runbooks, and maintainable solutions for smooth handoffs.
What you need
- Degree in computer science or equivalent practical experience.
- Three or more years of experience building and shipping production software, including being on-call and debugging failures.
- Proficiency in multiple programming languages such as Python, Node.js or TypeScript, and Go.
- Practical understanding of production model failure modes including provider rate limits, timeouts, retries, and streaming token accounting.
- Comfort deploying containerized services on Kubernetes with secrets management and configuration handling.
- Experience wiring observability and managing alerts using tools like Prometheus, Grafana, OpenTelemetry, Graylog, or ELK.
- High-concurrency experience handling message queues or event streams at real scale.
- Fluent written and verbal communication skills in both English and German.
Nice to have
- Experience with artificial intelligence voice assistants, STT/TTS, or LLM-based conversational systems.
- Building evaluation sets for a specific domain.
- Hands-on production experience with an LLM gateway such as LiteLLM, Portkey, or Kong AI Gateway.
- Familiarity with open-weight models and sovereign European cloud requirements.
Skills and tech stack
Questions candidates ask
What is the remote policy and location requirement for this role?
This is a hybrid role based in Munich, Germany, or requires a willingness to relocate. It also involves travel across Germany, Austria, and Switzerland.
What kind of team structure will I be working within?
You will work in a local Enterprise Sales Pod alongside a dedicated Enterprise Account Executive. You own the technical side while your partner owns the commercial relationship.
What tools and technologies will I use on a daily basis?
You will work with Python, TypeScript, Go, Kubernetes, containerized services, LLM gateways like LiteLLM, and observability stacks such as Prometheus, Grafana, and OpenTelemetry.
What is the interview or hiring process like?
The posting does not detail specific interview rounds, but highlights that you must be capable of running technical discovery, whiteboard sessions, and live troubleshooting with customer engineering teams.