About this role
Technical discovery, architecture design, proof-of-concept building, and production go-live happen daily as part of the Paris Enterprise Sales Pod. Working alongside a dedicated Enterprise Account Executive, this engineer embeds directly with enterprise clients to architect and ship production systems across voice, messaging, AI, and wireless infrastructure. Ownership covers the entire technical lifecycle without relying on a traditional support layer behind the scenes, making independent problem-solving and direct client collaboration essential.
Telnyx builds global connectivity infrastructure, multi-cloud IP networks, and edge technology through intuitive APIs. This particular role exists to scale enterprise business in the French market by delivering custom implementations that solve real-world problems. Ideal candidates bring strong software engineering foundations combined with the communication skills required to lead technical discussions with customer engineering teams and executives alike.
What you will do
- Embed directly with enterprise customers to evaluate communications workflows, integration challenges, and artificial intelligence use cases.
- Build and deploy custom implementations involving WebRTC, Telnyx APIs, and AI voice assistants.
- Run open-weight large language models in client environments using Telnyx Inference or deploy self-hosted stacks when sovereign cloud requirements apply.
- Deploy and operate LiteLLM as a model gateway to handle routing, load balancing, retries, fallbacks, and cost tracking.
- Wire production observability pipelines including metrics, logs, traces, dashboards, and alerting for shipped systems.
- Design model routing strategies for real-time voice workloads that balance latency, cost, and quality.
- Lead proof-of-concept projects, pilots, and production launches from initial whiteboard sessions to go-live stability.
What you need
- Hold a computer science degree or possess equivalent practical work experience.
- Bring at least three years of experience building and shipping production software that real users depend on.
- Demonstrate proficiency in multiple programming languages such as Python, Node.js, TypeScript, or Go.
- Maintain a practical understanding of production artificial intelligence failure modes, including rate limits, timeouts, and token accounting.
- Feel comfortable deploying containerized services on Kubernetes with proper secrets management and configuration.
- Possess hands-on experience wiring observability stacks like Prometheus, Grafana, OpenTelemetry, Graylog, or ELK.
- Communicate fluently in both English and French to run whiteboard sessions, live troubleshooting, and client escalations.
- Be based in Paris or willing to relocate for this hybrid role that includes travel across France.
Nice to have
- Experience with AI voice assistants, speech-to-text, text-to-speech, or LLM-based conversational systems.
- Experience building evaluation sets for specific business domains.
- Hands-on production experience with an LLM gateway such as LiteLLM, Portkey, Kong AI Gateway, or an in-house OpenAI-compatible proxy.
- Familiarity with the open-weight model landscape and the trade-offs between regional and multilingual models.
Skills and tech stack
Questions candidates ask
What is the remote or hybrid work policy for this role?
This is a hybrid role based in Paris, or requires willingness to relocate, with travel across France and time spent working on customer sites.
What does the team structure look like for this position?
You will work in a local Enterprise Sales Pod in Paris alongside a dedicated Enterprise Account Executive, acting as commercial partners who own the technical and business sides together.
What kind of support will I have from engineering?
You are self-sufficient by default as there is no engineering team behind you. You read the code and documentation, ask the customer directly, and make sound engineering judgment calls on your own.
What technologies and tools will I work with?
You will work with Python, Node.js, TypeScript, Go, Kubernetes, open-weight LLMs, LiteLLM, WebRTC, Telnyx APIs, and various observability stacks like Prometheus and Grafana.