Lead Value Engineer - Life Sciences

Key Responsibilities

• 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).
• Translate these complex, multi-tiered logistics requirements into innovative AI solutions that drive measurable impact.
• 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.
• Leverage cutting-edge AI technologies to rapidly build creative prototypes in customer hackathons, solving critical pain points in planning, sourcing, manufacturing, and distribution.
• 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).
• 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).

Requirements

• 8+ years of experience leading technical pre-sales and post-sales engagements specifically within Life Sciences, Pharmaceutical, or MedTech supply chains. This includes defining AI roadmaps, building compelling ROI/TCO business cases, and guiding technical implementations through to value realization.
• A deep understanding of supply chain business processes native to Life Sciences (such as Sales & Operations Planning (S&OP), Procure-to-Pay, Track & Trace, Cold Chain Management, or Quality Control/Batch Release) with the ability to translate high-level business needs into specific AI use cases.
• 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 (e.g., intelligent chatbots for supplier collaboration, automated extraction of data from complex customs or quality documents).
• Solid knowledge of Python and common ML libraries (such as LangChain, pandas, pydantic, sklearn, PyTorch) as well as data engineering tools and technologies for handling massive, siloed supply chain datasets.
• Strong presentation skills to both internal and external stakeholders (including supply chain executives and IT leaders), whether leading technical whiteboarding sessions or formal readouts and demos.

Nice to Have (Big Plus)

• 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.
• Familiarity with life sciences supply chain data standards and systems (e.g., GS1 EPCIS for traceability, SAP APO/IBP, Kinaxis).
• Experience in deploying and monitoring models at scale across major cloud platforms (AWS Bedrock, Azure AI, GCP Vertex).

Pay

The base salary range below is for the role in New York, based on a Full Time Schedule. Total compensation package will include base salary + bonus/commission + equity + benefits (health, dental, life, 401k, and paid time off). Please note that the base salary range is a guideline, and that the actual total compensation offer will be determined based on various factors, including, but not limited to, applicant's qualifications, skills, experiences, and location. The base salary range below is $157,000—$184,000 USD.