About this role
Telnyx is building a local Enterprise Sales Pod in Hong Kong, pairing an Account Executive with a Forward Deployed Engineer to establish a robust enterprise presence in the market. This role focuses on embedding directly with enterprise customers to design, architect, and ship production systems across voice, messaging, AI, and wireless infrastructure. The person joining this team will lead technical discovery, build proof-of-concepts, manage deployments, and ensure long-term stability for client implementations ranging from AI voice assistants to complex LLM gateways.
Operating as the sole technical counterpart to a commercial partner, this position requires high autonomy and direct engagement with customer engineering and executive teams. Candidates should possess a strong background in software engineering, cloud-native deployments, and hands-on client-facing technical delivery. Success in this capacity demands the ability to navigate high-stakes technical conversations, translate ambiguous customer challenges into reliable production outcomes, and communicate fluently in both English and Chinese.
What you will do
- Embed directly with enterprise customers to understand communications workflows, artificial intelligence use cases, and integration hurdles firsthand.
- Design and deploy custom implementations utilizing WebRTC, Telnyx APIs, and production-grade AI voice assistants.
- Run open-weight large language models in customer environments using Telnyx Inference or deploy self-hosted stacks when required.
- Deploy and operate LiteLLM as a model gateway in customer environments to handle routing, load balancing, retries, fallbacks, and rate limits.
- Instrument and govern model usage through the gateway to manage cost tracking, caching, logging, observability, and safety guardrails.
- Wire production observability including metrics, logs, traces, dashboards, and alerting for all shipped solutions.
- Collaborate directly with product and engineering teams to shape roadmaps based on field insights from Hong Kong and the broader Greater China and APAC region.
What you need
- Hold a computer science degree or possess equivalent practical software engineering experience.
- Bring three or more 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 practical understanding of production machine learning failure modes including rate limits, streaming, timeouts, and token accounting.
- Show comfort deploying containerized services on Kubernetes with proper secrets management, configuration, and upgrades.
- Possess experience wiring observability stacks such as Prometheus, Grafana, OpenTelemetry, Graylog, or ELK.
- Demonstrate excellent written and verbal communication skills in English and Chinese, including Cantonese or Mandarin.
Nice to have
- Experience with AI voice assistants, speech-to-text, text-to-speech, or LLM-based conversational systems
- Building evaluation sets for a specific domain
- Hands-on production experience with an LLM gateway such as LiteLLM, Portkey, Kong AI Gateway, or an in-house equivalent
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 Hong Kong, requiring work on customer sites and travel across Hong Kong and the broader Greater China and APAC region.
What kind of team structure will I be working within?
You will work as part of a local Enterprise Sales Pod alongside a dedicated Enterprise Account Executive, acting as their technical counterpart for discovery, architecture, and deployment.
What language proficiencies are required for the position?
Excellent written and verbal communication in English and Chinese, covering Cantonese and/or Mandarin, is required to run whiteboard sessions, live troubleshooting, and client escalations.
What is the focus of the technical work?
You will embed directly with enterprise customers to architect and ship production systems on the company network, focusing on voice, messaging, AI, and wireless workloads rather than just product demonstrations.