Applied AI Engineer

The AI agent space is moving fast. Most companies are still figuring out what to build. We already know, and we need someone to help us build it.

The Company

Sendbird is on a mission to build the AI workforce of tomorrow. For over a decade, we built the infrastructure behind conversations—chat, voice, video, messaging APIs—and became the #1 CPaaS platform for in-app communications. 4,000+ brands trust us. 7 billion messages flow through our platform every month. 300 million monthly active users.

We powered conversations for DoorDash, Match Group, Noom, Yahoo Sports, Rakuten, and thousands of others. We were good at what we did. Really good.

We also saw it early: AI would fundamentally reshape how businesses talk to customers. The infrastructure we'd spent a decade building would become commoditized. The value would move up the stack—into intelligence, into experience, into outcomes.

We had a choice: protect what we built, or reinvent ourselves.

We chose reinvention.

In December 2024, we made the full strategic pivot to AI-first customer experience. By February 2025, we'd launched our AI agent for enterprise CX—built on a decade of conversation data, now with intelligence on top. And in November 2025, we rebranded to delight.ai.

The name says it all. AI's real promise isn't efficiency or cost savings. It's giving customers back something they lost—the feeling of being truly understood and cared for. Not satisfied. Delighted.

The Product

Delight.ai is the AI concierge for customer experience. Most AI agents forget you the moment the conversation ends. Ours doesn't. Delight.ai builds memory over time, learns preferences, and connects context across every channel—chat, SMS, email, voice, WhatsApp—without losing the thread. We're building AI that makes customers feel understood, seen, and remembered.

Why Applied AI Engineer

The next wave of AI isn't chatbots answering FAQs. It's agents that reason, remember, and act, proactively and accurately, at scale. That's what we're building, and we need someone who can build the backend brain behind it.

This is where research meets production. You'll take the latest advances in LLMs, RAG, and agentic systems and turn them into features that enterprise customers depend on every day. Not demos. Deployed, scaled, real. You'll also be expected to use AI tooling aggressively in your own workflow, automating pipelines, wiring together APIs, and building with agentic CLIs so your output compounds beyond what any single engineer could ship alone.

The Role

You'll design and build the core intelligence layer of our AI agents: retrieval systems, tool-calling pipelines, voice integrations, and agentic reasoning capabilities. You'll also be expected to use AI-native development tools and automation to accelerate your own work and the team around you. The right person thrives at the intersection of applied research, production engineering, and building with AI.

You might be this person if:

• You read AI research papers and your first instinct is to run an experiment, not write a summary.
• You've shipped LLM-powered features to production and you know exactly what separates a reliable system from a promising prototype.
• You obsess over latency, accuracy, and failure modes. "Kind of works" isn't in your vocabulary.
• You use agentic CLIs like Claude Code to move fast, and you wire APIs, automation platforms, or custom scripts together to eliminate repetitive work for yourself and your teammates.
• You get uncomfortable when prompt engineering is the only lever being pulled. You want to go deeper.
• You can evaluate a voice AI vendor on quality, cost, and real-world performance without needing a framework to tell you how.
• You communicate technical tradeoffs clearly enough that product managers and engineers can make real decisions.

You need to have:

• 5 or more years of professional experience in machine learning, data science, or applied AI engineering.
• Hands-on experience with AI agent frameworks such as LangChain or LlamaIndex, tool use, or advanced prompt engineering.
• Working knowledge of voice AI technologies, including Speech-to-Text and Text-to-Speech, with the ability to evaluate s