Applied Engineer (solution Consultant) - Supply Chain

Celonis Celonis · Data AI · Milan, Italy · Value Engineering

This role is an Applied AI Engineer focused on pre-sales and solution consulting for enterprise customers, specifically in Supply Chain. The engineer will leverage Celonis' Process Intelligence platform with AI/ML partners (OpenAI, Databricks) to build prototypes and Proof-of-Value projects. Key responsibilities include understanding customer AI strategies, prototyping solutions using generative AI techniques like RAG, multi-agent orchestration, and fine-tuning, and ensuring successful implementation and value realization. The role involves integrating LLM/agent systems with enterprise data and compliance frameworks, and specializing in specific domains.

What you'd actually do

  1. 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
  2. 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
  3. 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
  4. 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.
  5. Ensure Successful Project Outcome: Applied AI Engineers stay involved with projects until agreed value & adoption thresholds are reached

Skills

Required

  • 3+ years of experience leading technical pre-sales
  • defining AI roadmaps
  • building compelling ROI/TCO business cases
  • 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
  • Understanding of business processes across sectors (such as Supply Chain or Finance)
  • 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)
  • data engineering tools and technologies
  • Strong presentation skills to both internal and external stakeholders (including executives)
  • Bachelor’s Degree required

Nice to have

  • 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)
  • Masters Degree in computer science, physics, engineering, mathematics or related fields, or equivalent work experience preferred.

What the JD emphasized

  • customer AI strategy
  • business critical problems
  • AI roadmaps
  • prototyping of machine learning and generative AI solutions
  • multi-agent orchestration
  • agentic systems
  • rigorous evaluations
  • guardrails

Other signals

  • customer-facing
  • pre-sales
  • prototyping
  • LLM/agent systems
  • RAG
  • tool use
  • guardrails
  • enterprise data integration