AI Forward Deployed Engineer

Overnox; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

Overnox is seeking a client-facing engineer to embed with enterprise customers, building and shipping production AI systems inside their environments. The role is hands-on and delivery-focused, bridging implementation and customer context to ensure reliable deployments.

You will embed with clients to scope, build, and integrate AI solutions within their infrastructure, translating business needs into technical AI implementations while navigating data, security, and integration constraints.

Responsibilities

• Embed with enterprise clients to scope, build, and integrate AI systems within their environment
• Ship production AI solutions that operate inside customer infrastructure and workflows
• Translate between business needs and technical AI implementations during delivery
• Navigate real-world enterprise constraints such as data, security, and integration requirements
• Act as the engineering lead who ensures AI actually works in the customer's setting

Job description

Role overview

A client-facing engineering role that embeds directly with enterprise customers to build, integrate, and ship production AI systems inside their environments. The work is hands-on and delivery-focused, centered on making AI work reliably in real-world enterprise settings rather than purely internal product development. It suits an engineer who thrives at the intersection of implementation and customer context.

Responsibilities

• Embed with enterprise clients to scope, build, and integrate AI systems within their environment
• Ship production AI solutions that operate inside customer infrastructure and workflows
• Translate between business needs and technical AI implementations during delivery
• Navigate real-world enterprise constraints such as data, security, and integration requirements
• Act as the engineering lead who ensures AI actually works in the customer's setting

Requirements

• Strong engineering background building and shipping production AI systems
• Comfort working directly inside client environments on integration-heavy work
• Ability to bridge technical implementation with enterprise customer needs
• Pragmatic, delivery-oriented approach to making AI systems work in practice