Associate Applied (AI) Value Engineer (DACH)

As an Applied (AI) Value Engineer you are pushing the envelope in solving business-critical problems for our customers.

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 showcasing the value of Celonis Process Intelligence tailored to customers and align our solutions with their AI strategy.
• Specialize in Domains or Industries: To scale knowledge across the organization specialize in domains (e.g. supply chain) and industries
• Business Impact Presentations: Articulate and quantify strategic business value for customers, delivering impactful presentations to senior executives to ensure successful value realization for customers.
• Ensure Successful Project Outcome: AI Solution Consultants stay involved with projects until agreed value & adoption thresholds are reached
• Best Practice Modeling: Contribute to the Global Value Engineering Center of Excellence by creating reusable collateral, best practices, and tools.

Requirements

• Degree in Engineering, Data Analytics/Science, Computer Science, Mathematics, or a related STEM field.
• Excellent academic performance
• Extensive internship experience or 1-2 years of full-time work experience. Ideally at the intersection of business and technology.
• Understanding of generative AI techniques like RAG, few shot learning, prompt/context 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.
• Good knowledge of Python and common ML libraries (such as LangChain, pandas, pydantic, sklearn, PyTorch) as well as data engineering tools and technologies.
• Excellent analytical and creative problem-solving skills, with the ability to apply technology to business challenges.
• A customer-centric mindset with a focus on delivering value.
• Confidence in presenting to diverse audiences.
• A proactive approach to problem-solving, with a willingness to learn from mistakes.
• Curiosity, self-motivation, a commitment to continuous learning, and the desire to thrive in a fast-paced, high-growth environment.
• Strong organizational skills with the ability to prioritize and execute on deadlines.
• Excellent communication skills and fluency in German and Englis h (must).
• Foundational business acumen and domain understanding, e.g. in areas such as Supply Chain.