Forward Deployed AI Sales Engineer

About Sapience AI

Sapience AI is the collective intelligence platform for professional communities. We sit above the CRMs, AMS platforms, and knowledge bases that organizations already run, and we turn the expertise scattered across them into something every member can search, act on, and share.

The intelligence a community needs is already inside it. Most organizations just cannot reach it. Knowledge lives in silos, in legacy systems, in the heads of a few experts, and in fragmented records no one can connect. We change that.

Our work is grounded in four commitments: technology elevates people and never replaces them, the best expertise is already inside the community, everything is built on trust, and every deployment is purpose-driven for the organization it serves.

Let’s achieve more, together.

Where this role sits

This is a customer-facing technical role that lives between what Sapience AI builds and what a community actually needs. You embed with prospects and new customers, you speak the language of their systems, and you prove that collective intelligence works on their data, not on a slide.

You are the person who turns MINERVA and the COGENT architecture into a working answer to a real problem in front of a real buyer. You run discovery, design the solution, stand up the proof, and stay close through the first deployment. Then you carry what you learned in the field straight back to product and engineering.

You partner with account executives, but you are not a quota-carrying seller and you do not own the commercial relationship. You own technical truth: whether it works, how it works, and what it will take to make it work here.

Why this role exists

Enterprise buyers of AI do not buy promises. They buy proof. The gap between an impressive demo and a signed deployment is filled by someone who can connect the platform to the customer’s messy reality and show that it holds up.

Professional communities run on trust, and their data is sensitive. A buyer needs to see that Sapience AI can reach the expertise trapped in their AMS, CRM, and knowledge base without breaking the trust their members place in them.

The Forward Deployed AI Sales Engineer closes that gap. You make the technology real, credible, and safe in the buyer’s own environment, so the decision rests on evidence rather than faith.

What you will own (Areas of Responsibility)

You hold eight areas of responsibility across the deal and deployment cycle. Each one is yours to set direction on, build, and measure.

1. Technical discovery and solution design

• Lead technical discovery with prospects: their systems, their data sources, their community, and the outcomes that would make this worth doing.
• Design the solution architecture that connects MINERVA and COGENT to the customer’s CRM, AMS, and knowledge sources, and map it to their success criteria.
• Scope pilots honestly, including what is in reach now, what depends on the roadmap, and where the real risks sit.

2. Demonstrations and proofs of value

• Build and deliver demonstrations grounded in the customer’s own use cases, not a generic script.
• Stand up proofs of value and pilots against real or representative data, and define the metrics that show whether the pilot succeeded.
• Translate pilot results into a clear, defensible story of value that leadership and technical evaluators both trust.

3. Deployment and integration in customer environments

• Configure and integrate Sapience AI in the customer environment, working through data access, identity, and connection to existing systems.
• Partner with forward deployed backend and full stack engineers to build the custom pieces a deployment needs.
• Own technical readiness through go-live, and hand off cleanly to customer success and support.

4. Technical trust, security, and governance assurance

• Answer security, privacy, and governance questions with precision, and represent how Sapience AI keeps community data protected and every deployment governed.
• Support security reviews, questionnaires, and architecture discussions with the customer’s technical and compliance teams.
• Make the guardrails visible, so buyers see that trust is built into the platform, not added later.

5. Field-to-product feedback loop

• Carry patterns from the field back to product and engineering: what customers ask for, where deployments get hard, and what would unlock the next set of deals.
• Tag feedback by source and frequency so product can act on evidence, not anecdote.
• Flag roadmap gaps early, before they become blockers in live deals.

6. Sales partnership and deal strategy

• Partner with account executives on technical qualification, deal strategy, and the path to a decision.
• Handle objections with candor, and know when to say a use case is not ready yet.
• Help shape the technical narrative that moves an evaluation forward.

7. Reusable assets and field enablement

• Turn repeated work into reusable demo environments, reference architectures, and technical playbooks.
• Share what works across the forward deployed team so the whole field gets faster.
• Keep technical talk tracks accurate as the platform evolves through each GR/GA milestone.

8. Community and data readiness assessment

• Assess whether a community’s data and systems are ready to produce value, and set honest expectations about what it will take.
• Identify the data sources that matter most for a given community and the order to bring them on.
• Advise on the practical steps a customer can take to get more from the platform over time.

AI-augmented ways of working

You work with AI in the room. You use Sapience AI’s own tools and other AI systems to accelerate discovery, generate solution drafts, prepare pilots, and answer technical questions faster, then apply your own judgment to what a customer actually needs.

The standard is human in partnership: AI accelerates the work, you own the judgment, the interpretation, and the call. The people who create the most value here are not the ones producing the most output. They are the ones turning evidence into clear, durable decisions.

What this role is not

To keep the boundary clear:

• This is not a quota-carrying sales role. You partner with account executives, but you do not own the number or the commercial relationship.
• This is not a support or implementation-only role. You work upstream in the deal and through first deployment, not ongoing ticket resolution.
• This is not a product management role. You feed the roadmap with field evidence, but you do not own delivery or the backlog.
• This is not a pure demo role. You are accountable for technical truth and working proof in the customer’s own environment.

What success looks like

We measure this role on outcomes the field and product teams can see:

• Technical win rate. Evaluations you lead convert because the proof is real and the risks are handled honestly.
• Time to proof. Pilots and proofs of value stand up quickly and answer the buyer’s real question.
• Clean deployments. First deployments go live on plan and hand off cleanly to customer success.
• Trusted in the room. Customer technical and security teams trust your answers, and objections get resolved rather than deferred.
• Field intelligence. Product and engineering act on the patterns you bring back from deployments.
• Reusable leverage. Repeated work becomes reference architectures and playbooks the whole team can use.

Who you are

Required qualifications

• Five or more years in sales engineering, solutions engineering, forward deployed engineering, or a 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.
• Strong hands-on ability to build demos and integrations, and to write code where a deployment needs it.
• Fluency with modern AI and ML systems, including LLMs, retrieval, and the practical limits of these systems in production.
• The ability to explain complex architecture clearly to both 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.

Preferred qualifications

• Experience deploying AI at customer sites in a forward deployed model.
• Familiarity with CRM, AMS, and knowledge-platform ecosystems, and with integrating them.
• 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.

How you work

• You lead with the customer’s real problem and name it plainly before reaching for a solution.
• You tell the truth about what works, what does not yet, and what it will take.
• You move fast, build working proof, and stay calm under technical pressure.
• You treat trust and data protection as first-order concerns, not afterthoughts.
• You share what you learn so the whole team gets better.

Skills & Competencies

• Technical discovery and solution architecture for AI deployments.
• Building demonstrations, prototypes, and proofs of value that map to customer outcomes.
• Integration across enterprise systems: APIs, data pipelines, identity, and access.
• Clear technical storytelling for mixed audiences of engineers, leadership, and security teams.
• Objection handling and technical qualification alongside sales.
• Security, privacy, and governance fluency for regulated and trust-sensitive buyers.
• Translating field patterns into structured product feedback.

Services & Tools Experience

• LLM and retrieval frameworks, vector stores, and orchestration tooling.
• Python and at least one of TypeScript or JavaScript for building integrations and demos.
• REST and GraphQL APIs, webhooks, and common enterprise integration patterns.
• CRM and AMS platforms (for example Salesforce and common association management systems) and knowledge-base systems.
• Cloud environments (AWS, GCP, or Azure) and containers.
• Demo automation, notebooks, and collaboration tooling.
• Familiarity with Sapience AI’s MINERVA platform and COGENT architecture (trained on the job).

Prior Experience & Background

• 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 that blends hands-on engineering with direct customer interaction.
• Exposure to trust-sensitive or regulated data environments is a plus.

Cross-functional partners

You work most closely with Sales, Applied AI, Solutions Architecture, and the forward deployed backend and full stack engineers who build alongside you. You partner with Product and Engineering through the field feedback loop, and with Customer Success on clean handoffs after go-live.

How we hire

We review every application, and we encourage you to apply even if you do not match every line above. Research shows that talented people, especially those from underrepresented communities, often hold back when they do not meet every qualification. If that is the only thing holding you back, apply anyway.

Sapience AI is an equal opportunity employer. We are committed to a workplace where everyone, regardless of background, has a voice in building what comes next.

Compensation

Base Salary: $180,000 - $190,000 + early equity

Generous health and wellness benefits

Sapience AI is an equal opportunity employer. We do not discriminate on the basis of gender, race or color, ethnicity or national origin, age, disability, religion, sexual orientation, gender identity or expression, veteran status, or any other protected characteristic. If you need an accommodation to complete our application process, let your recruiter know.