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 evaluated and iterated on LLM systems for accuracy, hallucination, latency, and cost You’ve built or integrated MCP servers or similar tool-use infrastructure You’ve influenced technical direction by earning trust, not by mandate You use modern tooling (including AI-assisted development workflows) to increase leverage, not outsource thinking (Note: we care about outcome and judgment, not how flashy your tools are.) Why AI at Narvar? Because post-purchase is one of the highest-leverage applications of conversational AI. We’re building AI agents where: The problem space is well-defined but complex — returns, claims, and support have clear business logic but messy real-world edge cases Scale is real — hundreds of millions of consumer interactions per year across major global retailers Impact is measurable — resolution rates, customer satisfaction, cost savings, not vanity metrics The work is greenfield — you’re not maintaining a legacy chatbot, you’re building the next generation You’ll help define where and how AI agent