Forward Deployed Engineer, LATAM (Remote)

The Role

We're looking for a Forward Deployed Engineer to join our LATAM team, with a focus on Bogotá, Mexico City, and São Paulo. In this remote, customer-facing role, you'll embed directly with enterprise customers across the region to architect and ship production systems on Telnyx's global network — voice, messaging, AI, and wireless — including self-hosted, open-weight LLM deployments that run inside customer environments. This isn't about demoing products; it's about building real solutions that work at scale.

This role would suit a technical professional who thrives at the intersection of engineering and customer success. You're equally comfortable debugging SIP traces as you are standing up a LiteLLM gateway with routing and failover across a customer's model providers, or leading a whiteboard session with a customer's engineering team. The ability to own outcomes from POC to production go-live is essential.

Customers across Latin America increasingly need Spanish- and Portuguese-first conversational AI, regional data residency, and flexible deployment options. A significant part of this job is making open-weight models perform reliably on infrastructure the customer controls. Professional fluency in Spanish or Portuguese, together with strong working proficiency in English, is required.

This is a LATAM-based remote role for candidates located in or near Bogotá, Mexico City, or São Paulo, with approximately 10–30% travel for deployments, workshops, and escalations across Colombia, Mexico, Brazil, and the wider region.

Responsibilities

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), and WebRTC

Deploy and operate open-weight LLMs such as Llama, Qwen, Mistral, DeepSeek, and gpt-oss in customer-controlled cloud, on-premises, and air-gapped environments

Deploy and operate LiteLLM as the model gateway in customer environments, providing a unified OpenAI-compatible interface across self-hosted models and hosted providers, with routing, load balancing, retries and fallbacks, rate limits, and per-team virtual keys

Instrument and govern LLM usage through cost tracking and budgets, caching, logging and observability using OpenTelemetry, Langfuse, or similar tools, and guardrails

Design model-routing strategies for real-time voice workloads, balancing latency, cost, reliability, and Spanish- or Portuguese-language quality

Make the build-vs-buy case between self-hosted open-weight models and hosted frontier APIs, while keeping customer application code portable across both

Adapt models to customer domains through prompt and RAG pipelines, LoRA/QLoRA fine-tuning, and evaluation harnesses for Spanish, Portuguese, and multilingual use cases

Lead POCs, pilots, and production launches from whiteboard to go-live

Own customer outcomes and remain engaged until the solution is live and stable

Collaborate directly with Product and Engineering to shape the roadmap based on field insights from the Latin American market

Create clear technical documentation, runbooks, and maintainable solutions for handoff in English and, where needed, Spanish or Portuguese

Troubleshoot and resolve complex integration issues alongside customer teams

Help customers address applicable privacy, security, telecommunications, and data-residency requirements, including Colombia's data-protection framework, Mexico's LFPDPPP, and Brazil's LGPD

What We Are Looking For

CS degree or equivalent experience

3+ years building software or doing technical consulting

Proficiency in multiple languages such as Python, Node.js, and Go — you're more dangerous in some than others

Hands-on experience running LiteLLM or a comparable LLM gateway such as Portkey, Kong AI Gateway, or an in-house proxy in production, including model definitions, proxy configuration, routing and fallback rules, and virtual key management

Practical understanding of production model-gateway challenges: provider rate limits and quotas, timeouts and retries, streaming, token accounting and cost attribution, and concurrency-related failure modes

Comfort deploying containerized services on Kubernetes, with secrets management, configuration, observability, and upgrades as part of the deployment story

Strong API fluency, event-driven thinking, and cloud-native instincts

Exposure to SIP, WebRTC, VoIP, or real-time voice and messaging systems

Ability to translate “it's not working” into root cause

Comfort working directly with customers and participating in high-stakes technical conversations

Professional fluency in Spanish or Portuguese

Professional working proficiency in English for internal collaboration, documentation, and work with Product and Engineering

Based in or near Bogotá, Mexico City, or São Paulo, with authorization to work in the applicable country

Willingness to travel approximately 10–30% across Latin America

Bonus Points For

Professional proficiency in both Spanish and Portuguese

Experience with AI voice assistants, STT/TTS, or LLM-based conversational systems, particularly for Latin American Spanish or Brazilian Portuguese

Fine-tuning and post-training experience: LoRA/QLoRA, distillation, preference tuning, or building evaluation sets for a specific domain

Experience with an inference-serving engine behind the gateway such as vLLM, SGLang, TGI, or Ollama, including sizing GPU capacity for self-hosted models

SQL proficiency with PostgreSQL, MySQL, or Oracle

ETL and data-wrangling experience

DevOps fundamentals including Docker, Kubernetes, and CI/CD

Background in telecom, CPaaS, contact centers, or high-growth SaaS

Experience designing private-cloud, sovereign-cloud, on-premises, or data-residency-sensitive architectures

Security mindset, including IAM, encryption, secrets management, and audit logging

#LI-RH1