Remote AI Process Deployment Engineer

Camunda; San Francisco (CA); Remote; USD 131,000 - 211,000; Full time

Benefits offered by this job

Remote work

Annual kickoff events

Health benefits

Pension plans

Learning budget

Job summary

Camunda is hiring for a Forward Deployed Engineer role focusing on deploying ProcessOS, a natural language-described business process platform, into production for enterprise customers. You will shepherd end-to-end deployment from the first process file to a live, improving system, shaping product patterns and field practices from day one.

You will work across Java services and React interfaces with a global team to measure, improve, and scale performance and fault tolerance in real-world

Qualifications

• Shipped solutions inside a large organisation with governance and resilience in mind.
• Ability to debug integrations, build and deploy Camunda clusters, and explain BPMN to engineers and business leads.
• Experience communicating across engineering, architecture and business leadership.
• Hands-on AI experience including context management, prompt engineering, and agent testing.

Responsibilities

• Own the end-to-end deployment journey from first process file to a running, improving production workflow.
• Expand customer deployments with measurable improvements within 12 months.
• Contribute to product by building features, agents, and skill files; fix bugs when found.

Skills

Enterprise production experience

Technical depth

Stakeholder communication

Practical AI experience

Comfort with ambiguity

Travel flexibility

Product adoption

Job description

Camunda is hiring for a Forward Deployed Engineer role focusing on deploying ProcessOS, a natural language-described business process platform, into production for enterprise customers. You will shepherd end-to-end deployment from the first process file to a live, improving system, shaping product patterns and field practices from day one.

You will work across Java services and React interfaces with a global team to measure, improve, and scale performance and fault tolerance in real-world