Responsibilities
- Understand clients' AI strategies and supply chain challenges in high-volume CPG, including predictive demand forecasting, inventory allocation, production scheduling, Order-to-Cash efficiency, and raw material volatility mitigation.
- Translate complex operational challenges into innovative AI solutions that drive measurable impact.
- Lead technical discovery and capability demonstrations during the pre-sales cycle within 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.
- Tackle heavily siloed legacy enterprise data by rapidly building creative prototypes in client hackathons to solve critical pain points across inventory deployment, shelf-availability, and DSD execution.
- Support enterprise clients in achieving tangible ROI from AI at scale by enabling a shift from rule-based automation to autonomous AI agents empowered by the Celonis Process Intelligence Platform.
- Architect and deliver secure, scalable LLM/agent systems with RAG, tools, and guardrails as part of end-to-end Proof-of-Value projects.
- Ensure seamless integration of AI systems with complex enterprise ERPs (e.g., SAP), Transportation Management Systems (TMS), and Warehouse Management Systems (WMS).
- Serve as the internal and external technical subject matter expert for CPG Supply Chains, scaling knowledge across the organization regarding fast-moving consumer goods (FMCG), trade spend management, and high-velocity manufacturing.
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.
- Experience 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.
- 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 for high-impact use cases.
- Solid knowledge of Python and common ML libraries such as LangChain, pandas, pydantic, sklearn, PyTorch.
- Proficiency with 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.
- 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
- 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).
- Working knowledge of tools in the LLM ecosystem such as LangChain, LlamaIndex, or other OSS packages.
- Experience in deploying and monitoring models at scale across major cloud platforms (AWS Bedrock, Azure AI, GCP Vertex).
Additional Information
- Visa sponsorship is not offered for this role.
