Lead Value Engineer
Lead technical pre- and post-sales engagements for Life Sciences clients, building AI solutions with Celonis Process Intelligence platform, LLMs, RAG, and agentic systems to optimize complex supply chains and demonstrate ROI to executives. Requires 8+ years in pharma/MedTech supply chain AI engagements, deep domain knowledge, Python/ML expertise, and Bachelor's degree.
About the job
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.
Skills
Python, LangChain, RAG, Prompt Engineering, Multi-Agent Orchestration, PyTorch, pandas, Sklearn, SAP, LLMs, Process Mining, Ai Prototyping
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