Senior Applied Value Engineer

Key Responsibilities

AI Discovery & Solutioning: Understand customers' AI strategies and sector-specific challenges (e.g., predictive maintenance, supply chain resilience, quality management). Find the best problem-solution fit and translate customer 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 supply chain, manufacturing, and warranty/quality 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., intelligent production scheduling, autonomous procurement), 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 data, identity protocols, and stringent manufacturing compliance frameworks.

Domain & Industry Leadership: Serve as the primary technical subject matter expert for the automotive manufacturing sector. Scale deep domain expertise across the organization to deliver high-value solutions for OEMs and Tier-1 suppliers.

Smart Factory & Assembly Automation: Champion Industry 4.0 initiatives. Specialize in assembly line automation, robotics, predictive maintenance, Industrial IoT (IIoT), and discrete manufacturing workflows for high-volume production.

EV & Advanced Vehicle Production: Act as a technical advisor on the transition to Electric Vehicles (EVs) and Software-Defined Vehicles (SDVs). Optimize battery production lines, connected factory ecosystems, and complex OEM/Tier-1 dynamics.

Sustainable Production & Factory Energy: Drive technical strategy for energy-efficient plant operations. Focus on integrating renewable energy into factory power grids and optimizing utility usage across the vehicle production lifecycle.

Requirements

• 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), as well as data engineering tools relevant to handling large-scale industrial data.
• Strong presentation and storytelling skills for both internal and external stakeholders (C-level executives and operational leaders).
• Capable of leading technical whiteboarding sessions, formal readouts, and live demos.
• Bachelor’s Degree required; Master's Degree in computer science, engineering, mathematics, or a 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 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).
• 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.