Staff AI Engineer: Scale LLMs, Orchestrate Agents & RAG

Wati Dot I O; Hong Kong; Vor Ort; HKD 900.000 - 1.500.000; Vollzeit

Benefits dieser Stelle

Direct access to founding team

Ownership of AI roadmap

Zusammenfassung

Wati is hiring a Staff AI Engineer to own LLM orchestration, RAG, and agent infrastructure at a 4B+ messages/year scale. You will lead architecture, deployment, and optimization of LLM-driven services, including multi-provider inference, RAG pipelines, multi-agent workflows, and voice AI, in a senior IC role with significant technical influence.

We seek a builder who bridges complex AI capabilities with massive production environments to ensure fast, reliable, and cost-effective AI across

Qualifikationen

• 5+ years in backend or infrastructure engineering with strong AI exposure.
• Expertise in at least one high-performance language and Python proficiency.
• Experience deploying LLM/NLP models to production at scale.

Aufgaben

• Architect and lead AI production stack across providers.
• Design scalable RAG pipelines and tool-calling infrastructure.
• Guide voice and multimodal AI integration across channels.
• Own data collection, cleaning, and model versioning pipelines.
• Optimize API costs, latency, and caching for large-scale workloads.
• Build AI quality assessment infrastructure with production metrics.
• Drive technology decisions and architectural patterns with leadership.

Kenntnisse

Go

Rust

C++

Python

LLM deployment

Data pipelines

Vector DBs

Kubernetes

Infra as Code

Multi-provider orchestration

Tools

GCP

AWS

Docker

LiveKit

WebRTC

Jobbeschreibung

Wati is hiring a Staff AI Engineer to own LLM orchestration, RAG, and agent infrastructure at a 4B+ messages/year scale. You will lead architecture, deployment, and optimization of LLM-driven services, including multi-provider inference, RAG pipelines, multi-agent workflows, and voice AI, in a senior IC role with significant technical influence.

We seek a builder who bridges complex AI capabilities with massive production environments to ensure fast, reliable, and cost-effective AI across