Associate Applied (AI) Value Engineer (UK) - Orbit Program

Celonis

Madrid, Spain

Full Time

Posted August 27, 2026

Climate Technology

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Job Description

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 in English (must).
Foundational business acumen and domain understanding, e.g. in areas such as Supply Chain.