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Deploys Camunda ProcessOS workflows for enterprise customers, builds custom AI agents and legacy-system connectors, debugs distributed integrations, and configures cloud and local clusters. The role drives customer adoption, documents deployment best practices, contributes production fixes and reusable patterns to the product, and trains customer teams for independent operation. It requires practical production AI experience, strong technical debugging skills, stakeholder communication, and comfort working without an established playbook.

As an AI Process Forward Deployed Engineer, you will deploy enterprise workflows, improve systems, drive adoption, and collaborate across teams to enhance AI integration in business processes.

Serve as the primary technical advisor for enterprise customers adopting Camunda. Develop technical adoption plans, provide hands-on platform and architecture guidance, conduct health checks, manage escalations, and advocate for customer needs. Collaborate with Customer Success, Consulting, Support, Product, Engineering, Sales, and Pre-Sales teams to drive outcomes. The role requires building trusted stakeholder relationships, explaining complex technology clearly, and traveling onsite up to 50% during busy periods.

Owns the strategy, design, implementation, governance, analytics, and continuous improvement of global sales compensation programs. Responsibilities include designing quotas, commissions, accelerators, and SPIFFs; configuring Xactly and Salesforce; managing launches, documentation, compliance, disputes, reporting, and training; and partnering with Sales, Finance, HR, and executive leadership to align incentives with business goals.

Build and operate scalable cloud data pipelines, warehouses, transformation layers, and ML production systems. Lead data infrastructure architecture, warehouse performance optimization, DataOps monitoring, CI/CD improvements, data quality testing, and large-scale data product delivery. Collaborate across teams, promote data stewardship, and support batch and streaming pipelines while deploying, serving, and monitoring machine learning models.