AI Process Forward Deployed Engineer

About the role

As an AI Forward Deployed ProcessOS Engineer, you will own the deployment journey from first process file to a working, improving system. You will hand off a functioning capability to the customer's team and ensure their team can operate independently.

Responsibilities

• Deploy and prove. Own the journey from first process file to production workflow.
  • At 90 days: at least one workflow running, the improvement loop active, and measurable fitness gains the customer can see.
  • At 12 months: 3–5 customers with viable deployments.
• Drive organizational adoption. Work across technical and business stakeholders to build understanding and trust, turning early deployments into lasting customer capability.
• Work at depth technically. Configure and run Camunda clusters locally and in cloud environments. Debug integration failures. Build custom agents where platform defaults don’t fit. Write connector logic that gets legacy enterprise systems talking to ProcessOS.
• Build the playbook. Document what good looks like as you discover it, and make the next implementation faster.
• Feed the product. Custom agents you write get contributed back. Integration patterns get codified. Production bugs you find, you often fix. Your field work is a direct input to ProcessOS.
• Hand off deliberately. Leave customers with a team that can operate independently, is trained on the platform, and acts on improvement results without you.

Requirements

Enterprise production experience: you’ve shipped something inside a large organization and stayed to see it operate. You know why governance and resilience matter at scale.

Technical depth: You can debug an integration failure, write a custom agent against a legacy API, stand up a Camunda cluster from scratch, and explain generated BPMN to both engineers and business leads.

Stakeholder communication: you work effectively across engineering, architecture, and business leadership and adapt how you communicate without losing precision.

Practical AI experience: you’ve used AI to solve real problems in production – not just experimented. You understand context management, prompt engineering, agent testing, and integration tradeoffs.

Comfort with ambiguity: you make sound judgment calls without a playbook and document what you learn.

Ability and/or willingness to use our product

What You Bring

• Nice-to-haves: Experience in a customer-facing engineering or professional services role, Familiarity with regulated industries such as financial services, healthcare, or insurance, Production experience with agentic systems or AI-integrated workflows, Contributions to a shared platform or open source project that others built on.