The Role
We're building a local Enterprise Sales Pod in Hong Kong — one AE and one Forward Deployed Engineer (FDE), working together to build a Telnyx enterprise business in your market. You'll embed directly with enterprise customers to architect and ship production systems on Telnyx's global network — voice, messaging, AI, and wireless.
This isn't about demoing products. It's about building real solutions that work at scale.
You'll work side-by-side with a dedicated Enterprise AE — not a demo resource, but a commercial partner who owns the named-account list, builds executive relationships, and closes the deal. You own the technical side: discovery, architecture, POC, and production go-live. You win together. No throw-over-the-wall.
The Pod Model
Enterprise AE — owns the named-account list, builds executive relationships, gets in the room, owns commercial qualification and close, expands the account.
Forward Deployed Engineer — owns technical discovery, designs the architecture, builds the POC when gated, gets the first workload live, removes blockers and finds the next workload.
We sell the workload, not the SKU. Customers buy a production outcome — an AI contact center, a SOC/NOC agent, global communications, connected mobility, or enterprise inference — that runs on Telnyx primitives (voice, messaging, numbers, wireless, Voice AI, inference, agents, connectivity). You lead with the outcome conversation, not a product menu.
What You'll Do
• Embed with enterprise customers to understand their communications workflows, AI use cases, and integration challenges firsthand
• Build and deploy custom implementations: AI Voice Assistants, Telnyx APIs (Voice, Messaging, Fax, Wireless), WebRTC
• Run open-weight LLMs in customer environments — consume GLM, Kimi, DeepSeek, Qwen, and MiniMax through Telnyx Inference (OpenAI-compatible API; there's no model infrastructure for you to run), or deploy self-hosted stacks (Llama, Mistral, and region-specific models like CKIP-LLO, FlagAlpha, or other Cantonese/Chinese-language sovereign models) behind your own serving engine when air-gapped or sovereign-cloud requirements demand it
• Deploy and operate LiteLLM as the model gateway in customer environments: a unified OpenAI-compatible interface across self-hosted open-weight models and hosted providers, with routing, load balancing, retries and fallbacks, rate limits, and per-team virtual keys
• Instrument and govern LLM usage through the gateway — cost tracking and budgets, caching, logging and observability (OpenTelemetry, Langfuse, or similar), and guardrails — so customers can see and control what their AI workloads are doing
• Wire production observability for everything you ship — metrics, logs, traces, dashboards, and alerting (e.g. Prometheus + Grafana for metrics, OpenTelemetry for traces, Graylog or ELK for logs — or the customer's existing stack) — so you and the customer's ops team both know what "healthy" looks like and what pages whom when it isn't
• Design model routing strategies for real-time voice workloads, balancing latency, cost, and quality, with sane fallback behavior when a provider degrades
• Make the build-vs-buy case between self-hosted open-weight models and hosted frontier APIs, and keep the customer's application code portable across both
• Adapt models to customer domains: prompt and RAG pipelines and evaluation harnesses for English, Cantonese, and Mandarin language use cases and Hong Kong/APAC-specific regulatory environments (PDPO, data localization, Mainland China cross-border data transfer)
• Lead POCs, pilots, and production launches from whiteboard to go-live
• Own customer outcomes — stay engaged until the solution is live and stable
• Collaborate directly with Product and Engineering to shape the roadmap based on field insights from Hong Kong and the broader Greater China/APAC region
