Customer-Facing AI Full-Stack Engineer (Forward Deployed)

Sapience AI Corporation; Seattle (WA); On-site; USD 156,000 - 168,000; Full time

Benefits offered by this job

Generous health benefits

Early equity

Job summary

Sapience AI is seeking a Forward Deployed AI Full Stack Engineer to deliver end-to-end customer deployments across the data layer, services, and member-facing interfaces. You will work with MINERVA and COGENT architectures to craft workflows, screens, and integrations that fit the customer’s world.

You will own deployments in customer environments, move fast from problem to running product, and ensure adoption while feeding learnings back to improve the core platform for future communities.

Qualifications

• Five+ years building full stack applications.
• Hands-on experience with AI/ML systems, LLMs, and retrieval.
• Experience delivering end-to-end customer deployments.
• Strong frontend and backend development skills.
• Experience integrating with CRM/AMS and knowledge bases.
• Security, permissions, and data protection focus.

Responsibilities

• Deliver end-to-end solutions for customer deployments.
• Build member-facing interfaces and workflows on MINERVA.
• Integrate deployment logic with COGENT architecture.
• Ensure data is current, accurate, and searchable.
• Instrument adoption and feed learnings back to product.
• Collaborate with forward deployed sales and backend engineers.
• Hand off to customer success with proper documentation.

Skills

Full stack

Frontend framework

AI/ML systems

End-to-end delivery

Security & data protection

Systems integration

Tools

Python

Node.js

LLM orchestration

REST

GraphQL

Cloud platforms

Vector/Graph DBs

SQL/NoSQL

Job description

Sapience AI is seeking a Forward Deployed AI Full Stack Engineer to deliver end-to-end customer deployments across the data layer, services, and member-facing interfaces. You will work with MINERVA and COGENT architectures to craft workflows, screens, and integrations that fit the customer’s world.

You will own deployments in customer environments, move fast from problem to running product, and ensure adoption while feeding learnings back to improve the core platform for future communities.