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Salary: USD 196,000 - 230,000 / annual
Celonis is seeking a Lead Applied Value Engineer to drive enterprise AI solutions for strategic customers in the High Tech vertical. You will partner with major CPG brands and retailers to solve business-critical challenges—from supply chain resilience and inventory optimization to margin expansion and sustainable operations.
In this role, you will leverage Celonis's Process Intelligence platform combined with leading AI/ML technologies (Microsoft, OpenAI, Databricks) to build innovative solutions that deliver measurable impact. You'll work across the full customer lifecycle: conducting AI discovery sessions, designing compelling ROI/TCO business cases, prototyping solutions during customer hackathons, architecting proof-of-value projects, and ensuring successful implementation and adoption.
Key responsibilities include:
- AI Discovery & Solutioning: Understand customer AI strategies and sector-specific challenges (demand forecasting, out-of-stock prevention, shelf placement, trade spend analytics) and translate requirements into needle-moving solutions.
- Pre- and Post-Sales Execution: Lead technical discovery and capability demonstrations during pre-sales; remain deeply involved post-sale to guide implementation and ensure value realization.
- Hackathons & Prototyping: Rapidly build creative prototypes using cutting-edge AI to solve critical pain points in inventory routing, fulfillment, and promotional alignment.
- Agentic Process Transformation: Shift customers from rule-based automation to autonomous AI agents empowered by Process Intelligence (e.g., autonomous inventory replenishment, intelligent deduction management).
- Proof Projects: Architect and execute business-critical proof-of-value projects, delivering secure, scalable LLM/agent systems with RAG, tools, and guardrails integrated with enterprise retail data and privacy frameworks.
- Domain & Industry Leadership: Serve as the primary technical subject matter expert for the High Tech sector, scaling deep domain expertise across the organization.
- Smart Warehouse & Fulfillment Operations: Champion modern distribution initiatives including WMS optimization, RFID tracking, micro-fulfillment centers, dynamic order routing, and fulfillment center workflows.
- Omnichannel & DTC Transformation: Act as technical advisor on seamless omnichannel and Direct-to-Consumer execution, optimizing order-to-cash cycles, returns processing, dynamic pricing, and loyalty integrations.
- Sustainable Supply Chain & Eco-Fulfillment: Drive technical strategy for sustainable retail operations, focusing on Scope 3 emissions tracking, cold-chain efficiency, perishable food waste reduction, and sustainable packaging workflows.
Requirements:
- 8+ years leading end-to-end technical pre-sales and post-sales engagements within CPG, retail, or e-commerce. Proven ability to define AI roadmaps, build ROI/TCO business cases, and guide implementations to value realization.
- Deep understanding of High Tech business processes and in-depth experience in domains such as Inventory Management, Supply Chain, Trade Promotion, Category Management, or Loss Prevention.
- Solid knowledge of Python and common ML libraries (LangChain, pandas, pydantic, sklearn, PyTorch), plus data engineering tools for handling large-scale POS, transactional, and inventory data.
- Strong presentation and storytelling skills for C-level executives, VPs of Merchandising, and Supply Chain Leaders; capable of leading technical whiteboarding sessions, formal readouts, and live demos.
- Bachelor's Degree required; Master's Degree in computer science, business analytics, engineering, mathematics, or related field (or equivalent work experience) preferred.
Nice to have:
- Hands-on experience building agentic systems using LLM orchestration, RAG, function calling, and prompt engineering with rigorous evaluations for enterprise consumer environments.
- Working knowledge of OSS packages like LangChain or LlamaIndex.
- Experience deploying and monitoring models at scale across AWS Bedrock, Azure AI, GCP Vertex; familiarity with enterprise data structures (SAP, Salesforce Commerce Cloud, Snowflake, POS formats).
- Expertise in GenAI techniques (RAG, few-shot learning, multi-agent orchestration, multimodal understanding) for use cases like automated customer service workflows, intelligent product catalog enrichment, or automated promotion generation.
