Forward Deployment AI Engineer

• Location:
  
  New York City (Hybrid)
• Compensation: $200,000 - $250,000 OTE
• Travel:
  Up to 30% for on-site customer engagements

A high-growth SaaS business trusted by tens of thousands of IT professionals across 120+ countries, with serious traction, a clear product vision, and AI at the absolute core of what is being built. This is not a business bolting AI onto an existing product. It is a business built around the idea that IT operations can and should run autonomously, and they are already proving it ked by strong investment, growing fast in North America, and with a product that genuinely changes how enterprise IT teams operate day to day.

AI is reshaping IT management, and that creates a gap between what the platform can do and what customers know how to unlock. As a Forward Deployment AI Engineer, you close that gap. You will embed directly with enterprise customers, own deployments end-to-end, build LLM-powered workflows, and drive real adoption at the coalface. This is not a support role or a solutions sales role.

This is for someone with serious engineering instincts, a natural customer presence, and a bias toward building things that actually reach production and deliver outcomes.

• Owning end-to-end deployment for enterprise accounts from kickoff through architecture, configuration, integration, and handoff
• Customising AI automations and core product capabilities to match each customer s environment and workflows
• Building and deploying LLM-powered workflows that solve real operational problems and reach production
• Leading technical discovery with enterprise IT stakeholders, translating architecture, constraints, and success criteria into concrete deployment plans
• Identifying repeatable patterns and codifying them into playbooks and templates so every deployment improves on the last
• Acting as the technical authority during customer engagements including on-site visits, bridging escalations between the customer, Support, and Engineering
• Feeding field insights directly back into Product and CX as the closest person to where the platform meets reality

• Strong engineering foundation in Computer Science, Software Engineering, Mathematics, Physics, or Machine Learning via degree or equivalent experience
• Hands-on experience building with LLMs, a solid grasp of where models fail, and something real already shipped
• Solid understanding of ML fundamentals including evaluation, training pipelines, and decomposing business problems into ML-solvable framing
• 2+ years in a customer-facing technical role across implementation, professional services, or solutions engineering
• Familiarity with IT infrastructure including Windows/Mac endpoints, Active Directory, Azure AD, and networking basics
• Willingness to travel up to 50% for on-site enterprise engagements

If this sounds like your next move, get in touch.