Senior AI Engineer responsible for developing conversational AI for post-purchase resolutions at Narvar. Designing systems for returns, claims, and customer service experiences in a remote setting.
Responsibilities
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
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
Requirements
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
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
Have strong programming skills in Python or TypeScript
Have experience building and integrating APIs and backend services
Are comfortable reasoning about evaluation, safety, and reliability in non-deterministic systems
Take initiative naturally and are comfortable operating with ambiguity.
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