Applied Value Engineer

Key Responsibilities

• AI Discovery & Solutioning: Understand customers AI strategy and business critical challenges. As Celonis product & domain expert, find the best problem-solution fit and translate customer requirements into innovative solutions that move the needle
• Hackathons & Prototyping: Think out of the box, have a „can-do“ attitude and don’t shy away from complex problems. Leverage cutting edge AI technologies to rapidly build creative prototypes in customer hackathons solving business critical problems
• Agentic Process Transformation: Support our customers in achieving real ROI out of AI deployments at scale enabling a fundamental shift in business operations from traditional, rule-based automation to the use of autonomous AI agents empowered by our Celonis Process Intelligence Platform
• Proof projects: End-to-end execution of business-critical Proof-of-Value projects, incl. architecture and deliver secure, scalable LLM/agent systems with RAG, tools, and guardrails; integrating with enterprise data, identity, and compliance frameworks.
• Ensure Successful Project Outcome: Applied AI Engineers stay involved with projects until agreed value & adoption thresholds are reached
• Specialize in Domains or Industries: To scale knowledge across the organization Applied AI Engineers specialize in domains (e.g. supply chain) and industries

Requirements

• 7+ years of experience leading technical pre-sales, including defining AI roadmaps, building compelling ROI/TCO business cases and prototyping of machine learning and generative AI solutions.
• Understanding of generative AI techniques like RAG, few shot learning, prompt engineering, multi-agent orchestration, multimodal understanding, or fine-tuning that are used to build high-impact use cases like intelligent chatbots and automated text processors.
• Understanding of business processes across sectors (such as Supply Chain or Finance) with the ability to translate high-level business needs into specific AI use cases.
• Good knowledge of Python and common ML libraries (such as LangChain, pandas, pydantic, sklearn, PyTorch) as well as data engineering tools and technologies.
• Strong presentation skills to both internal and external stakeholders (including executives), whether whiteboarding sessions or formal readouts and demos.
• Bachelor’s Degree required, Masters Degree in computer science, engineering, mathematics or related fields, or equivalent work experience preferred.

Nice to have (big plus)

• Hands-on experience building agentic systems using LLM orchestration, RAG, function calling, and prompt engineering, while ensuring safety through rigorous evaluations and guardrails.
• Working knowledge of tools in the LLM ecosystem such as LangChain, LlamaIndex, or other OSS packages.
• Experience in deploying and monitoring models at scale across major cloud platforms (AWS Bedrock, Azure AI, GCP Vertex)