Senior Value Engineer - Life Sciences

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

Senior Value Engineer for Life Sciences at Celonis, focusing on building and implementing AI solutions using Celonis' Process Intelligence platform and AI/ML partners. The role involves understanding client needs, prototyping AI solutions, and ensuring value realization for AI deployments at scale, particularly in supply chain optimization. It emphasizes agentic process transformation and proof-of-value projects involving LLM/agent systems with RAG and guardrails, integrated with enterprise systems and regulatory frameworks.

What you'd actually do

  1. Understand the client's overarching AI strategy and the distinct supply chain challenges across both their MedTech portfolios (e.g., mitigating global raw material shortages, optimizing supply chains, managing inventories, or accelerating quality batch releases). As a Celonis product and life sciences domain expert, translate these complex, multi-tiered logistics requirements into innovative AI solutions that drive measurable impact.
  2. 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 in the supply chain are successfully reached.
  3. Think out of the box, have a „can-do“ attitude, and don’t shy away from complex, fragmented supply chain networks. Leverage cutting-edge AI technologies to rapidly build creative prototypes in customer hackathons, solving critical pain points in planning, sourcing, manufacturing, and distribution.
  4. 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., autonomous inventory rebalancing or intelligent shipment exception handling).
  5. 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 ERPs (e.g., SAP), Quality Management Systems (QMS), and strict regulatory frameworks (FDA, EMA, GxP).

Skills

Required

  • Life Sciences supply chain expertise
  • Technical pre-sales and post-sales experience
  • Generative AI techniques (RAG, prompt engineering, multi-agent orchestration, fine-tuning)
  • Python and ML libraries (LangChain, pandas, pydantic, sklearn, PyTorch)
  • Data engineering tools
  • Presentation skills
  • Experience with enterprise ERPs (SAP) and QMS

Nice to have

  • Agentic systems building (LLM orchestration, RAG, function calling, prompt engineering)
  • Rigorous evaluations for regulated environments
  • Life sciences supply chain data standards and systems (GS1 EPCIS, SAP APO/IBP, Kinaxis)
  • LLM ecosystem tools (LangChain, LlamaIndex)

What the JD emphasized

  • 5+ years of experience leading technical pre-sales and post-sales engagements specifically within Life Sciences, Pharmaceutical, or MedTech supply chains.
  • Deep understanding of supply chain business processes native to Life Sciences
  • Expertise in generative AI techniques like RAG, few-shot learning, prompt engineering, multi-agent orchestration, multimodal understanding, or fine-tuning used to build high-impact use cases
  • Solid knowledge of Python and common ML libraries
  • Hands-on experience building agentic systems using LLM orchestration, RAG, function calling, and prompt engineering, while ensuring safety through rigorous evaluations suited for highly regulated (GxP) life sciences environments.

Other signals

  • AI/ML is core to the role
  • Building agentic systems
  • Integrating AI with enterprise systems
  • Focus on ROI and value realization of AI