Senior Applied Value Engineer – CPG & Retail
$160,000–$190,000 year
HybridNew York, United States
Job Summary
Lead technical discovery and solutioning for CPG and retail clients, translating sector-specific challenges into innovative AI-driven solutions that drive measurable ROI. Architect Proof-of-Value projects and agentic systems using LLMs, RAG, and enterprise data protocols to optimize supply chains, inventory management, and omnichannel fulfillment workflows. Partner with executives on pre- and post-sales engagements, guiding implementation of sustainable supply chain strategies and autonomous process transformations. Deliver secure, scalable systems that integrate with ERP, WMS, and POS platforms to industrialize AI for global brands.
Required Qualifications
- 8+ years leading end-to-end technical pre-sales and post-sales engagements within the CPG, retail, or e-commerce space
- Proven ability to define AI roadmaps, build compelling ROI/TCO business cases, and guide technical implementations to value realization
- Deep understanding of retail and CPG business processes
- 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)
- Strong presentation and storytelling skills for both internal and external stakeholders (C-level executives, VPs of Merchandising, and Supply Chain Leaders), capable of leading technical whiteboarding sessions, formal readouts, and live demos
- Bachelor's Degree
- Master's Degree in computer science, business analytics, engineering, mathematics, or a related field (or equivalent work experience)
Desired Qualifications
- 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 major cloud platforms (AWS Bedrock, Azure AI, GCP Vertex) and 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) to build high-impact use cases like automated customer service workflows, intelligent product catalog enrichment, or automated promotion generation
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