Requirements
- Experience: 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.
- Domain Expertise: Deep understanding of manufacturing business processes. In-depth experience in domains such as Asset Management, Supply Chain, Quality Control, or Capital Projects, 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 industrial data.
- Communication Skills: 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.
- Education: Bachelor’s Degree required; Master's Degree in computer science, 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 highly regulated industries.
- LLM Ecosystem: Working knowledge of OSS packages like LangChain or LlamaIndex.
- Cloud & IoT: 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.
- Generative AI: 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.
