Senior AI Engineer

Narvar is Growing! We’re building Navi — Narvar’s agentic AI that automates post-purchase resolution for the world’s leading retailers. Hundreds of millions of consumers interact with Narvar every year. Navi is our agentic AI that resolves delivery issues, returns, and refunds through natural conversation — powered by IRIS and 74 billion consumer touchpoints.

We're looking for senior AI engineers to own this system end-to-end: architecture, model selection, production operations. You'll help decide what gets built and how.

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

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

Bonus points

These aren’t hard requirements, but strong indicators:

You’ve worked in startup or high-ownership environments

You’ve built and operated AI systems in production, including monitoring and incident response

You’ve eval