Staff AI Engineer

Started as a WhatsApp team inbox in 2020, Wati has evolved into an AI-powered customer engagement platform that goes beyond a single channel. Designed for businesses that sell, support, and grow through conversations, Wati observes customer intent in real time, decides the next best revenue action, and executes it across marketing, sales, and support — on WhatsApp, Instagram, Facebook, TikTok, SMS, and more.

Trusted by over 16,000 customers across 190+ countries, Wati simplifies complex operations and business conversations with a unified inbox, no-code automation, and our intelligent AI layer, Astra.

Proudly backed by Tiger Global, Sequoia Capital, DST Global, and Shopify, and recognised as a Premium Partner of Meta and Google.

We’re hiring a Staff AI Engineer to own LLM orchestration, RAG, and agent infrastructure at 4B+ messages/year scale.

Our platform processes over 4 billion messages per year across 100+ countries. Your mission is to build the robust, scalable, and intelligent systems that turn conversation data into real-time, intelligent customer experiences.

In this role, you will lead the architecture, deployment, and optimization of our LLM-driven services — including multi-provider inference orchestration, RAG pipelines, multi-agent workflows, and voice AI. This is a senior IC role with significant technical influence across the AI stack.

We need a "builder" who can bridge the gap between complex AI capabilities and massive-scale production environments, ensuring our AI is fast, reliable, and cost-effective.

• →Core LLM Infrastructure: Architect and lead our AI production stack, including multi-provider LLM gateway optimization, token budget management, and low-latency inference routing across OpenAI, Gemini, and other providers.
• →Agentic AI & RAG: Design and implement scalable RAG (Retrieval-Augmented Generation) systems, multi-step AI agent workflows, and tool-calling infrastructure (MCP), ensuring high accuracy and reliability in customer interactions.
• →Voice & Multimodal AI: Lead the evolution of our voice AI layer (WebRTC/realtime) and cross-channel agent coordination across text, voice, and connected messaging platforms.
• →AI Production Lifecycles: Own the "Engineering-to-AI" loop: building automated pipelines for data collection, cleaning, fine-tuning orchestration, and model versioning.
• →Performance & Cost Optimization: Continuously optimize API costs, token budgets, latency, and caching strategies to ensure our 4-billion-message scale remains sustainable and performant.
• →Evaluation & Benchmarking: Build the infrastructure for systematic AI quality assessment, identifying failure modes and ensuring model improvements are grounded in real-world production metrics.
• →Technical Roadmap: Drive technology decisions in close collaboration with engineering leadership, selecting frameworks and architectural patterns that will define our AI future.

• Systems Expert: 5+ years of professional experience in backend or infrastructure engineering. Mastery of at least one high-performance language (Go, Rust, or C++) and deep proficiency in Python.
• AI Deployment Mastery: Proven track record of taking LLMs/NLP models from experiments to high-traffic production. You understand multi-provider orchestration, prompt engineering at scale, and model drift management.
• Data Pipeline Experience: Strong experience building data pipelines for AI workloads, including document processing, embedding generation, and vector search.
• Product-Minded Engineer: You don’t just build for the sake of tech; you understand how AI performance impacts customer outcomes and business value.
• Autonomous Builder: You thrive in environments with high ambiguity and can design, code, and deploy complex systems independently.
• Experience with vector databases (e.g., Qdrant, Milvus, Pinecone) and RAG architecture patterns.
• Familiarity with agentic frameworks, tool-calling protocols (MCP, function calling), or multi-agent orchestration.
• Experience with real-time voice/audio AI pipelines (WebRTC, LiveKit, or similar).
• Infrastructure-as-Code experience with GCP/AWS, Docker, and Kubernetes.