We're hiring a Founding Forward Deployed Engineer to work directly with enterprise customers to deploy, tune, and scale Arist's AI agents—making them work brilliantly in the context of each customer's organization.

This role sits at the intersection of engineering, AI, and customer outcomes. You'll embed with customers post-implementation to ensure our AI-driven needs analysis and agentic experiences deliver real, measurable value—not just a successful launch.

For large enterprise deals where we're selling consulting-level AI services, you may also deploy from the very beginning alongside the customer's team to design and stand up agentic workflows from day one.

If you've been a principal or lead solutions engineer and are looking for a step up in scope, ownership, and impact—especially at the frontier of applied AI—this role is designed for you.

What You'll Do

• Make AI Agents Work for Customers
• Work directly with customers post-implementation to deploy, configure, and optimize Arist's AI agents in their environment.
• Gather and map the right datasets so AI outputs are accurate, safe, and relevant to each customer's context.
• Define and apply guardrails that balance flexibility with trust and data integrity.
• Iterate with customer stakeholders until the experience feels native to their organization—and just works.
• Deploy Agentic Workflows for Strategic Accounts
• For large deals where Arist is selling consulting-level AI services, embed with the customer from the start to design and stand up agentic workflows.
• Act as a hands-on technical partner—understanding the customer's organizational context, data landscape, and business goals to build AI experiences that deliver measurable outcomes.
• Bridge the gap between what AI can do and what the customer actually needs it to do.
• Build the FDE Playbook
• Define and document the FDE engagement model: when to engage, what data is needed, what guardrails apply, and how to measure success.
• Create reusable frameworks for AI needs analysis deployment by customer type, industry, and data maturity.
• Feed insights back to product and engineering to make deployments faster, safer, and more repeatable.
  
  

You will report directly to the Co-founder and Chief Product Officer.

How The Role Evolves

Phase 1: Learn, Deploy, and Define the Playbook (0–6 months)

• Go hands-on with existing customers to deploy and tune AI agents across a range of enterprise environments.
• Document what works: required data by customer type, guardrails and safety considerations, recommended approaches by use case.
• Identify patterns that can be productized versus what requires ongoing FDE support.
• Begin shaping the FDE engagement model for strategic and consulting-level accounts.
  
  

Phase 2: Own the Motion and Build the Team (6–18 months)

• Own the full FDE motion from post-implementation AI deployment through ongoing optimization and expansion.
• Serve as the technical face of strategic AI engagements for Arist's largest customers.
• Build and lead a scalable FDE team.
• Create tight feedback loops with product and engineering to turn customer insights into platform improvements.
  
  

Why This Role Is Different

This is not a post-sale support role. You'll be at the frontier of applied AI in enterprise learning—defining how AI agents get deployed, tuned, and trusted in real organizational contexts.

You'll own cross-functional outcomes tied directly to customer retention, expansion, and product adoption. You'll be a founding leader of the FDE function, with the mandate to build both the team and the operating model.

What We're Looking For

• Senior-level experience as a Forward Deployed Engineer, Solutions Engineer, ML Engineer, or similar customer-facing technical role with hands-on AI/ML work.
• Strong understanding of AI/ML systems, prompt engineering, data pipelines, and how to make models work well in real-world contexts.
• Comfort working directly with enterprise customers on ambiguous, high-impact AI deployment challenges.
• Experience defining guardrails, safety frameworks, and quality standards for AI systems in production.
• Ability to think both tactically (making an agent work for one customer) and strategically (building playbooks that scale).
• Experience collaborating closely with product, engineering, sales, and customer success.