Camunda Lead

Job Description

Our Mission is to Simplify Life. We are looking to Simplify and automate complex decision-making for customer-centric industries, like Utilities, Financial Services, Logistics, and commerce, that drive the world's economies and you have the chance to join the revolution. We are trying to solve huge challenges in today's enterprise that are directly impacting the employee and customer experience.

At Avertra, the Lead Camunda Engineer will play a pivotal role in the seamless integration of AI capabilities with Camunda's BPMN and DMN modeling. By understanding intricate business requirements and establishing best practices, the role focuses on achieving a harmonious synergy between AI understanding and BPM orchestration. This position involves leading the engineering team, architecting business processes, and ensuring the Fikra platform remains at the forefront of innovation.

Main Job Responsibilities

• AI Training: Lead the efforts in training the AI on Camunda BPMN and DMN to ensure optimal utilization of the AI capabilities.
• Best Practices Establishment: Define and implement best practices for prompting the AI tool. This includes understanding business context and architecting complex business processes and decision-making models.
• Reverse Engineering: Collaborate with the Senior Camunda Engineer to reverse engineer current MiCustomer apps on Mendix. Define both common capabilities and client-specific nuances.
• Reusable Components: Architect and develop reusable Camunda components, connectors. Integrate these with OpenAI GPT, making the AI aware of the developed components, established best practices, and the overarching process landscape.
• Dynamic Decision Architecture: Lead the development of a dynamic decision architecture in Camunda that can be exposed as configuration layers, ensuring flexibility and adaptability.
• Collaboration: Work closely with product owners, architects, and other stakeholders to translate requirements into BPM solutions, ensuring alignment with AI capabilities.
• Documentation: Consistently maintain clear documentation of processes, design decisions, training protocols, and best practices.
• Participate in AI Model testing, provide feedback for model retraining and enhance training data.