Senior Applied Value Engineer – Automotive Manufacturing
$140,000–$175,000 year
HybridNew York, United States
Job Summary
Lead technical discovery and solutioning for automotive manufacturing clients, translating supply chain, quality, and warranty challenges into innovative AI-driven solutions. Drive the full customer lifecycle from pre-sales demonstrations to post-sale implementation, ensuring value realization through agentic process transformation and secure LLM/agent system architecture. Rapidly prototype creative solutions during hackathons to address critical pain points in assembly automation, predictive maintenance, and sustainable factory energy optimization. Serve as the primary subject matter expert for OEMs and Tier-1 suppliers, guiding technical whiteboarding sessions and live demos for C-level executives.
Required Qualifications
- 4+ years leading end-to-end technical pre-sales and post-sales engagements within manufacturing and production
- Proven ability to define AI roadmaps, build compelling ROI/TCO business cases, and guide technical implementations to value realization
- Deep understanding of manufacturing business processes
- In-depth experience in domains such as Asset Management, Supply Chain, Quality Control, or Capital Projects
- 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 and operational leaders)
- Bachelor's Degree
- Must be able to lift 50 lbs
Desired Qualifications
- Master's Degree in computer science, engineering, mathematics, or a related field (or equivalent work experience)
- Hands-on experience building agentic systems using LLM orchestration, RAG, function calling, and prompt engineering, with rigorous evaluations for highly regulated industries
- 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 IT/OT convergence and industrial IoT data structures
- Expertise in GenAI techniques (RAG, few-shot learning, multi-agent orchestration, multimodal understanding, fine-tuning) to build high-impact use cases like automated engineering document processing or intelligent diagnostic chatbots
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