Forward Deployed AI Sales Engineer

Sapience AI Corporation•Seattle, WA

7d•$180,000 - $190,000

About The Position

Sapience AI is hiring a customer-facing technical professional to help prospects and new customers evaluate and deploy its collective intelligence platform. The role connects Sapience AI’s MINERVA platform and COGENT architecture to customers’ CRM, AMS, and knowledge systems, demonstrating value using customer-specific use cases and data. The position partners with account executives but does not carry a quota or own the commercial relationship; it owns technical truth, proof, and readiness for deployment. The role spans technical discovery, solution design, demonstrations, proofs of value, customer-environment integration, and technical readiness through go-live. It also supports security and governance assurance, communicates field insights to product and engineering, develops reusable field assets, and assesses customer community and data readiness. The role works closely with Sales, Applied AI, Solutions Architecture, forward deployed engineers, Product, Engineering, and Customer Success. The successful candidate uses Sapience AI’s tools and other AI systems to accelerate discovery and preparation while applying independent judgment. Success is measured through technical win rate, time to proof, clean deployments, customer trust, actionable field intelligence, and reusable technical assets. The position requires willingness to travel to customer sites as deals require. Compensation is a base salary of $180,000–$190,000 plus early equity, with health and wellness benefits.

Requirements

At least five years of experience in sales engineering, solutions engineering, forward deployed engineering, or another customer-facing technical role in enterprise software or AI.
A track record of winning technical evaluations and standing up proofs of value against real customer data.
Hands-on ability to build demonstrations and integrations and write code when deployments require it.
Fluency with modern AI and machine learning systems, including LLMs, retrieval, and their practical production limitations.
Ability to explain complex architecture clearly to technical evaluators and non-technical leadership.
Comfort with data integration, identity, security, and enterprise deployment concerns.
Willingness to travel to customer sites as deals require.
Technical discovery and solution architecture skills for AI deployments.
Experience building demonstrations, prototypes, and proofs of value aligned to customer outcomes.
Integration experience with enterprise systems, APIs, data pipelines, identity, and access.
Clear technical storytelling for engineering, leadership, and security audiences.
Ability to handle objections and support technical qualification alongside sales.
Security, privacy, and governance fluency for regulated and trust-sensitive buyers.
Ability to translate field patterns into structured product feedback.
Experience with LLM and retrieval frameworks, vector stores, and orchestration tooling.
Python and at least one of TypeScript or JavaScript.
Experience with REST and GraphQL APIs, webhooks, and common enterprise integration patterns.
Experience with cloud environments such as AWS, GCP, or Azure, and containers.
Prior forward deployed, sales engineering, or solutions engineering experience at an enterprise software or AI company.
Experience owning the technical side of enterprise evaluations from discovery to pilot.
A background combining hands-on engineering with direct customer interaction.

Nice To Haves

Experience deploying AI at customer sites in a forward deployed model.
Familiarity with CRM, AMS, and knowledge-platform ecosystems and their integration.
Understanding of knowledge graphs, neuro-symbolic systems, or agentic AI.
Domain understanding of professional communities, associations, or membership-driven organizations.
Experience supporting enterprise security and compliance reviews.
Experience with demo automation, notebooks, and collaboration tooling.
Exposure to trust-sensitive or regulated data environments.
Familiarity with Sapience AI’s MINERVA platform and COGENT architecture; training is provided on the job.

Responsibilities

Lead technical discovery into prospects’ systems, data sources, communities, and desired outcomes.
Design solution architectures connecting MINERVA and COGENT to customer CRM, AMS, and knowledge sources, mapped to success criteria.
Scope pilots candidly, including current capabilities, roadmap dependencies, and risks.
Build and deliver demonstrations based on customer use cases.
Set up proofs of value and pilots using real or representative data, and define success metrics.
Explain pilot results as a clear, defensible value story for leadership and technical evaluators.
Configure and integrate Sapience AI in customer environments, including data access, identity, and connections to existing systems.
Partner with forward deployed backend and full stack engineers on custom deployment work.
Own technical readiness through go-live and hand off to customer success and support.
Answer customer security, privacy, and governance questions and support security reviews, questionnaires, and architecture discussions.
Bring structured field feedback to product and engineering, tagging feedback by source and frequency and flagging roadmap gaps early.
Partner with account executives on technical qualification, deal strategy, objections, and the technical narrative for evaluations.
Create reusable demo environments, reference architectures, technical playbooks, and field enablement materials.
Assess community data and system readiness, prioritize relevant data sources, set expectations, and advise customers on practical steps to improve value over time.
Use Sapience AI and other AI systems to accelerate discovery, solution drafting, pilot preparation, and technical research while exercising independent judgment.
Travel to customer sites as deals require.