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Celonis is seeking an Applied Value Engineer to work with strategic enterprise customers, architecting and delivering cutting-edge AI solutions on the Celonis Process Intelligence platform. You will combine process data, business knowledge, and AI intelligence to create digital twins that unlock operational clarity and transformational outcomes.
In this role, you will execute end-to-end discovery and solutioning for mission-critical customer challenges. You'll translate domain expertise and product knowledge into bespoke, state-of-the-art AI solutions targeting industry-specific operational pain points. Your work spans technical discovery, prototyping, and building secure, scalable LLM/agent systems with RAG, tools, and guardrails integrated into enterprise data, identity, and compliance frameworks.
You will drive Proof-of-Value (PoV) projects from conception through value realization, maintaining active technical and advisory involvement with strategic accounts. This includes demonstrating business impact to C-suite executives, ensuring successful implementation and adoption, and codifying domain-specific solutions into reusable methodology and assets.
Required qualifications include 2+ years of executive-facing presales and value engineering experience with proven PoV execution, ROI/TCO modeling, and ability to pitch visionary solutions. You need solid domain and industry expertise (supply chain, finance, etc.) and the ability to speak the language of business leaders. Technical requirements include Python proficiency, API design, SQL, data integration, and hands-on experience with ML/AI libraries (PyTorch, TensorFlow, scikit-learn, XGBoost, Hugging Face Transformers).
You must have hands-on experience with agentic AI systems including LLM orchestration, tool use/function calling, RAG, agents, and prompt engineering. Production ML/LLMOps experience is essential, including model deployment, monitoring, and lifecycle management across cloud services (AWS Bedrock, Azure AI, GCP Vertex AI). Understanding of cloud reference architectures, identity and access, data governance, privacy, and compliance is required.
Additional strengths include applied knowledge of optimization, statistical analysis, reinforcement learning, and forecasting. Strong presentation skills to internal and external stakeholders, including executives, are critical. Fluent English and native-level local language with exceptional executive presence required. Master's degree in computer science, engineering, or mathematics preferred.
