Lead Value Engineer

Key Responsibilities

• Understand client's AI strategy and supply chain challenges in Life Sciences (MedTech portfolios), including mitigating raw material shortages, optimizing supply chains, managing inventories, and accelerating quality batch releases.
• Translate complex logistics requirements into innovative AI solutions using Celonis Process Intelligence platform combined with AI/ML technologies.
• Drive full customer lifecycle: lead technical discovery and demonstrations in pre-sales, guide post-sale implementation, adoption, and value realization.
• Rapidly build creative prototypes in customer hackathons using cutting-edge AI to solve pain points in planning, sourcing, manufacturing, and distribution.
• Support customers in achieving ROI from AI deployments by shifting to autonomous AI agents empowered by Process Intelligence (e.g., autonomous inventory rebalancing, intelligent shipment exception handling).
• Execute end-to-end Proof-of-Value projects: architect and deliver secure, scalable LLM/agent systems with RAG, tools, guardrails; integrate with ERPs (SAP), QMS, and regulatory frameworks (FDA, EMA, GxP).
• Serve as technical subject matter expert for Life Sciences Supply Chain, scaling knowledge on pharmaceutical manufacturing and logistics processes.

Requirements

• 8+ years leading technical pre-sales and post-sales engagements in Life Sciences, Pharmaceutical, or MedTech supply chains, including defining AI roadmaps, building ROI/TCO cases, and guiding implementations to value realization.
• Deep understanding of Life Sciences supply chain processes (S&OP, Procure-to-Pay, Track & Trace, Cold Chain Management, Quality Control/Batch Release) and ability to translate business needs into AI use cases.
• Expertise in generative AI techniques: RAG, few-shot learning, prompt engineering, multi-agent orchestration, multimodal understanding, fine-tuning for high-impact use cases.
• Solid knowledge of Python, ML libraries (LangChain, pandas, pydantic, sklearn, PyTorch), and data engineering tools for massive supply chain datasets.
• Strong presentation skills for technical whiteboarding, readouts, and demos to supply chain executives and IT leaders.
• Bachelor’s Degree required; Master’s in computer science, supply chain management, engineering, mathematics, or related fields preferred.

Nice-to-Haves

• Hands-on experience building agentic systems using LLM orchestration, RAG, function calling, prompt engineering, with safety evaluations for GxP regulated environments.
• Familiarity with life sciences supply chain data standards and systems (GS1 EPCIS, SAP APO/IBP, Kinaxis).
• Working knowledge of LLM ecosystem tools (LangChain, LlamaIndex, other OSS packages).
• Experience deploying and monitoring models at scale on AWS Bedrock, Azure AI, GCP Vertex.