The opportunity
Celonis is the trusted platform to industrialize Enterprise AI. At our core is the Celonis Context Model — which combines process data, business knowledge, and intelligence into a living digital twin of the enterprise that AI can actually understand, turning AI's operational blind spots into operational clarity.
What you'll do
Discovery & AI Solutioning: Translate domain expertise, product knowledge, and specific customer requirements into flawless project execution. You will execute technical discovery, allowing you to prototype and build bespoke, state-of-the-art AI solutions that precisely target nuanced, industry-specific operational pain points.
AI Solution Development: Drive the end-to-end execution of business-critical Proof-of-Value (PoV) projects. Deliver secure, scalable LLM/agent systems with RAG, tools, and guardrails; integrating with enterprise data, identity, and compliance frameworks.
Value Selling & Realization: Maintain active technical and advisory involvement with strategic accounts, ensuring the project remains on track until agreed-upon value, adoption thresholds, and operational transformations are achieved.
Domain & Industry Specialization: Leverage domain expertise to build industry-specific E2E solutions. Contribute to codifying this work into reusable methodology and assets to accelerate team-wide time-to-value.
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.
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.
What they're looking for
- 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.
- 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.
