Applied Value Engineer

Join our team as an Applied Value Engineer, where you will tackle business-critical problems for our strategic global accounts. You will diagnose their strategy and operational challenges, architect and prototype AI solutions, and ensure successful implementation and value realization. Your expertise in domain-specific use cases, AI solution development, and value selling will be key to your success in this role.

Responsibilities

• Diagnosing customer strategies and operational challenges to build tailored use cases.
• Architecting and prototyping AI and technical solutions on the Celonis platform, demonstrating their business impact.
• Driving the end-to-end execution of business-critical Proof-of-Value (PoV) projects, ensuring successful implementation and value realization.
• Strong presentation skills to both internal and external stakeholders (including executives), whether whiteboarding sessions or formal readouts and demos. Flawless English with exceptional executive presence. Fluent in Dutch would be a preference but not essential
• Executive-Facing Presales & Value Engineering (2+ years): Demonstrated experience supporting the technical close and driving compelling AI transformation narratives. Requires a proven background in accountable PoV execution, ROI/TCO modeling, and showcasing the "Art of the Possible" by pitching visionary solutions to hidden problems
• Production ML/LLMOps: Experience with model deployment, monitoring, MLOps/LLMOps, and lifecycle management across cloud services (AWS Bedrock, Azure AI, GCP Vertex AI)
• AI Architecture & Security: Understanding of cloud reference architectures, identity and access, data governance, privacy, and compliance

Requirements

• Degree: Masters Degree in computer science, engineering, mathematics, business engineering, business information or related fields , or equivalent work experience preferred
• Domain & Industry Expertise: Solid knowledge of core business processes and industry landscapes (e.g., supply chain, finance). Ability to speak the language of business leaders and connect operational bottlenecks to strategic AI solutions
• Math & Operations Research: Applied knowledge of linear/nonlinear optimization, statistical analysis, reinforcement learning, and forecasting is a plus
• Software/Data Engineering: Python proficiency, API design, SQL, data integration, and ML/AI libraries (PyTorch, TensorFlow, scikit-learn, XGBoost, Hugging Face Transformers)
• Agentic AI Systems: Hands-on experience with agentic AI setups including LLM orchestration, tool use/function calling, RAG, agents, prompt engineering, or agentic patterns for enterprise workflows