Senior AI Engineer

Day-to-day

• Design and build conversational AI agents for returns, claims, and customer service experiences
• Own agent systems from architecture → implementation → evaluation → production operations
• Build RAG / context graph retrieval pipelines that ground agent responses in real company and customer data
• Design agent orchestration for multi-step workflows that interact with identity, risk, order, and loyalty systems
• Create evaluation frameworks to measure task completion, accuracy, safety, and user satisfaction
• Implement guardrails and safety mechanisms — content moderation, hallucination detection, graceful fallbacks
• Integrate conversational experiences across web, mobile, SMS, and email channels
• Make real decisions around prompt design, model selection, latency/cost/quality tradeoffs, and failure modes
• Collaborate with product, design, and ML teams to build systems that are technically sound and product-aware

What We’re Looking For

We care more about judgment and ownership than credentials.

You’re likely a strong fit if you:

• Have shipped conversational AI or agent-based systems used by real users in production
• Have built production systems on top of LLM APIs and agent frameworks — not just prompt playgrounds, but real integrations involving tool orchestration, context management, and reliability at scale
• Have a point of view on model selection tradeoffs — when to use frontier APIs vs. open-weight models (Qwen, Llama, Mistral), and understand the cost, latency, privacy, and capability tradeoffs of each
• Understand prompt engineering beyond basics: structured outputs, few-shot learning, chain-of-thought, tool calling
• Have built context graph pipelines that go beyond naive retrieval — entity resolution, relationship modeling, and dynamic context assembly from structured and unstructured data
• Have designed agent architectures that use function calling, tool execution, or multi-step reasoning