Senior Value Engineer, Consumer Packaged Goods (CPG)

Key Responsibilities

• AI Discovery & Solutioning
• Understand the clients' overarching AI strategies and the distinct supply chain challenges inherent to high-volume CPG (e.g., predictive demand forecasting, inventory allocation, production scheduling, Order-to-Cash efficiency, and mitigating raw material volatility).
• Translate these complex, physical-world challenges into innovative AI solutions that drive measurable impact.
• Pre- and Post-Sales Execution
• Actively drive the full customer lifecycle. Lead technical discovery and capability demonstrations during the expansion/pre-sales cycle within various manufacturing, commercial, and supply chain business units.
• Remain deeply involved post-sale to guide implementation, ensuring agreed value, margin efficiency, and adoption thresholds are successfully reached.
• Hackathons & Prototyping
• Think out of the box with a "can-do" attitude, tackling heavily siloed legacy enterprise data. Leverage cutting-edge AI technologies to rapidly build creative prototypes in client hackathons, solving critical pain points across inventory deployment, shelf-availability, and DSD execution.
• Agentic Process Transformation
• Support these enterprise clients in achieving tangible ROI from AI at scale. Enable a fundamental shift from traditional, rule-based automation to autonomous AI agents empowered by the Celonis Process Intelligence Platform (e.g., autonomous inventory rebalancing, intelligent shipment exception handling, or automated trade promotion deductions and dispute resolution).
• Proof Projects
• End-to-end execution of business-critical Proof-of-Value projects. Architect and deliver secure, scalable LLM/agent systems with RAG, tools, and guardrails, ensuring seamless integration with complex enterprise ERPs (e.g., SAP), Transportation Management Systems (TMS), and Warehouse Management Systems (WMS).

Requirements

• 5+ years of experience leading technical pre-sales and post-sales engagements specifically within highly complex Supply Chain, CPG/FMCG, or Food & Beverage environments.
• This includes defining AI roadmaps, building compelling ROI/TCO business cases for large-scale manufacturing and distribution networks, and guiding technical implementations through to value realization.
• Deep understanding of supply chain business processes native to high-volume consumer goods (such as Order-to-Cash, Procure-to-Pay, Sales & Operations Planning (S&OP), Direct Store Delivery (DSD), or Trade Promotion Optimization) with the ability to translate high-level operational 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 logistics routing assistants, or automated extraction of data from complex vendor contracts and shipping documentation).
• 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, high-velocity transactional enterprise datasets.
• Strong presentation skills to both internal and external stakeholders (including supply chain executives, logistics directors, and enterprise IT leaders), whether leading technical whiteboarding sessions or formal readouts and demos.

Education

• Bachelor’s Degree required; Master's Degree in computer science, supply chain management, industrial engineering, mathematics, or related fields, or equivalent work experience preferred.

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 enterprise-grade supply chain environments.
• Familiarity with CPG-specific enterprise architecture (e.g., SAP APO/IBP, Blue Yonder, Manhattan, or major ERP/WMS platforms).
• Experience in deploying and monitoring models at scale across major cloud platforms (AWS Bedrock, Azure AI, GCP Vertex).