Applied Value Engineer - Production

Celonis Celonis · Data AI · New York, NY · Value Engineering

This role focuses on applying AI and ML, specifically generative AI techniques like LLMs, RAG, and agents, to solve business-critical problems for enterprise customers in the Production Vertical (Automotive, Manufacturing, Energy). The engineer will prototype, demonstrate, and guide the implementation of these solutions, bridging IT and OT systems, and ensuring ROI and adoption at scale. The role involves both pre-sales and post-sales execution, with a strong emphasis on building agentic systems and integrating them with enterprise data and compliance frameworks.

What you'd actually do

  1. AI Discovery & Solutioning: Understand customers' AI strategy and sector-specific challenges (e.g., predictive maintenance, outage management, supply chain resilience, Quality Management). As a Celonis domain expert you are tasked to , find the best problem-solution fit and translate customer requirements into innovative solutions that move the needle.
  2. Pre- and Post-Sales Execution: Actively drive the full customer lifecycle. Lead technical discovery and capability demonstrations during the pre-sales cycle, and remain deeply involved post-sale to guide implementation, ensuring agreed value and adoption thresholds are successfully reached.
  3. Hackathons & Prototyping: Think out of the box, have a „can-do“ attitude, and don’t shy away from complex operational problems. Leverage cutting-edge AI technologies to rapidly build creative prototypes in customer hackathons, solving critical pain points specific to Supply Chain, Manufacturing, Grid Operations and Warranty and Quality..
  4. Agentic Process Transformation: Support our customers in achieving real ROI out of AI deployments at scale, enabling a fundamental shift from traditional, rule-based automation to the use of autonomous AI agents empowered by our Celonis Process Intelligence Platform (e.g., intelligent field service routing or autonomous procurement).
  5. Proof Projects: End-to-end execution of business-critical Proof-of-Value projects. This includes architecting and delivering secure, scalable LLM/agent systems with RAG, tools, and guardrails, while seamlessly integrating with enterprise data, identity protocols, and stringent energy compliance frameworks.

Skills

Required

  • Python
  • LangChain
  • pandas
  • pydantic
  • sklearn
  • PyTorch
  • data engineering tools
  • large-scale industrial data handling
  • business process understanding
  • industry knowledge in Energy and / or Manufacturing
  • Asset Management
  • Field Services
  • Supply Chain
  • Capital Projects
  • Trading
  • technical pre-sales
  • post-sales engagements
  • AI roadmaps
  • ROI/TCO business cases
  • technical implementations
  • presentation skills
  • C-level executive communication

Nice to have

  • agentic systems
  • LLM orchestration
  • RAG
  • function calling
  • prompt engineering
  • evaluations
  • guardrails
  • highly regulated industries
  • LlamaIndex
  • OSS packages
  • deploying and monitoring models at scale
  • AWS Bedrock
  • Azure AI
  • GCP Vertex
  • IT/OT convergence
  • industrial IoT data structures
  • generative AI techniques
  • few-shot learning
  • multi-agent orchestration
  • multimodal understanding
  • fine-tuning
  • automated processing of engineering documents
  • regulatory filings
  • intelligent diagnostic chatbots

What the JD emphasized

  • 4+ years of experience leading end-to-end technical pre-sales and post-sales engagements specifically within manufacturing and production
  • Deep understanding of business processes native and industry knowledge in Energy and / or Manufacturing
  • Solid knowledge of Python and common ML libraries (such as LangChain, pandas, pydantic, sklearn, PyTorch)
  • Hands-on experience building agentic systems using LLM orchestration, RAG, function calling, and prompt engineering, while ensuring safety through rigorous evaluations and guardrails suited for highly regulated industries.

Other signals

  • AI/ML capabilities
  • Process Intelligence Graph
  • industrialize AI
  • AI deployments at scale
  • autonomous AI agents
  • LLM/agent systems
  • generative AI techniques