Senior AI Engineer

🕒 March 18

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Narvar

WebsiteLinkedInAll Job Openings

201 - 500 employees

Founded 2012

📦 Logistics

📣 Marketing

💼 Consulting

💰 $30M Series C on 2018-08

Logistics • Marketing • Consulting

Narvar is a company that specializes in creating intelligent post-purchase experiences for ecommerce businesses. Their platform provides a range of services to enhance customer engagement and retention, from setting clear delivery expectations and proactive multi-channel messaging to seamless returns and exchanges. Narvar's solutions are designed to build loyalty by personalizing the consumer journey and optimizing reverse logistics, ultimately converting and retaining customers while increasing revenue and reducing costs. The company boasts a powerful network and integrations with popular platforms like Shopify, Zendesk, and Salesforce, and supports over 2,000 retailers globally. Narvar's services cater to a variety of industries including apparel, electronics, and beauty, providing exceptional post-purchase experiences through actionable insights and data-driven interactions across their ecosystem.

📋 Description

• 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

🎯 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 • 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.