Forward Deployed Engineer – Delivery Prediction, AI Systems

November 4

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Narvar

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eCommerce • SaaS • Retail

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.

201 - 500 employees

Founded 2012

🛍️ eCommerce

☁️ SaaS

🛒 Retail

💰 $30M Series C on 2018-08

📋 Description

• Collaborate with customer and internal teams to design, integrate, and deploy predictive models that improve delivery-date accuracy. • Build and maintain data pipelines that power real-time insights and reporting. • Use AI agents and automation to replace manual customer workflows and diagnostics. • Own customer-facing delivery performance dashboards and reporting systems. • Debug integrations, latency, and data quality issues across multiple environments. • Operate as both engineer and consultant, translating real-world needs into durable, scalable systems.

🎯 Requirements

• 5–8 years of experience in software engineering, implementation, or technical consulting. • Strong programming fundamentals (Python preferred) and a curiosity to grow into AI/ML systems. • Proven track record working directly with customers or external partners on technical deployments. • Ability to debug and integrate APIs, ETL pipelines, or data mapping workflows. • Familiarity with modern AI tools, LLM APIs, workflow automation, or prompt engineering. • Excellent communication skills and empathy across business and engineering stakeholders. • A bias for learning, iteration, and delivering measurable outcomes.