Lead Applied Value Engineer - High Tech

Role Description

As a Lead Applied Value Engineer, you will push the envelope in solving business-critical problems for strategic customers within our High Tech Vertical. You will partner with our most important clients to understand their unique objectives—from global supply chain resilience and trade promotion optimization to inventory shrink reduction and large-scale margin expansion programs. By combining the world’s leading Process Intelligence (PI) platform with technologies from top AI and ML partners (Microsoft, OpenAI, Databricks), you will build innovative solutions that drive measurable impact.

Using our PI platform, we feed operational context to AI, bridging ERP, WMS, POS, and e-commerce platforms so it understands our customers’ complex commercial realities. This enables CPG brands and retailers to industrialize AI and unlock real ROI at scale. You will prototype these solutions, demonstrate their value to CPG and retail executives, and ensure successful implementation, adoption, and value realization to expand our footprint across the global retail and consumer ecosystem.

Key Responsibilities

• AI Discovery & Solutioning: Understand customers' AI strategies and sector-specific challenges (e.g., demand forecasting, out-of-stock prevention, shelf placement, trade spend analytics). Find the best problem-solution fit and translate business requirements into innovative, needle-moving solutions.
• Pre- and Post-Sales Execution: Drive the full customer lifecycle. Lead technical discovery and capability demonstrations during pre-sales, and remain deeply involved post-sale to guide implementation and ensure agreed value and adoption thresholds are met.
• Hackathons & Prototyping: Leverage cutting-edge AI technologies to rapidly build creative prototypes during customer hackathons. Solve critical pain points specific to inventory routing, fulfillment, and promotional alignment with a proactive, "can-do" approach.
• Agentic Process Transformation: Shift customers from traditional, rule-based automation to autonomous AI agents empowered by Process Intelligence (e.g., autonomous inventory replenishment, intelligent deduction management), ensuring real ROI on AI deployments.
• Proof Projects: Architect and execute business-critical Proof-of-Value projects. Deliver secure, scalable LLM/agent systems with RAG, tools, and guardrails, integrating seamlessly with enterprise retail data, identity protocols, and consumer data privacy frameworks.
• Domain & Industry Leadership: Serve as the primary technical subject matter expert for the High Tech sector. Scale deep domain expertise across the organization to deliver high-value solutions for global brands, mega-retailers, and distribution partners.
• Smart Warehouse & Fulfillment Operations: Champion modern distribution initiatives. Specialize in warehouse management system (WMS) optimization, RFID inventory tracking, micro-fulfillment centers, dynamic order routing, and fulfillment center workflows for high-velocity retail operations.
• Omnichannel & DTC Transformation: Act as a technical advisor on the transition to seamless omnichannel and Direct-to-Consumer (DTC) execution. Optimize order-to-cash cycles, customer returns processing, dynamic pricing models, and loyalty program integrations.
• Sustainable Supply Chain & Eco-Fulfillment: Drive technical strategy for sustainable retail operations. Focus on tracking Scope 3 emissions, optimizing cold-chain efficiency, reducing perishable food waste, and streamlining sustainable packaging workflows across the distribution network.

Requirements

• Experience: 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.
• Domain Expertise: Deep understanding of the High Tech business processes. In-depth experience in domains such as Inventory Management, Supply Chain, Trade Promotion, Category Management, or Loss Prevention, with the ability to translate strategic requirements into impactful solutions.
• Technical Proficiency: Solid knowledge of Python and common ML libraries (LangChain, pandas, pydantic, sklearn, PyTorch), as well as data engineering tools relevant to handling large-scale POS, transactional, and inventory data.
• Communication Skills: 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.
• Education: Bachelor’s Degree required; Master's Degree in computer science, business analytics, engineering, mathematics, or a related field (or equivalent work experience) preferred.

Nice to Have

• Agentic Systems: Hands-on experience building agentic systems using LLM orchestration, RAG, function calling, and prompt engineering, with rigorous evaluations for enterprise consumer environments.
• LLM Ecosystem: Working knowledge of OSS packages like LangChain or LlamaIndex.
• Cloud & Enterprise Stack: 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).
• Generative AI: 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.