FDE （Forward Deployed Engineer）

Role Purpose: For industrial product and engineering project design scenarios, apply AI, complex data analytics, predictive modeling, and optimization to requirement understanding, solution analysis, parameter recommendation, risk prediction, and knowledge reuse, delivering verifiable, integrable algorithms, data models, and application prototypes.

Key Responsibilities

· Research AI applications in engineering requirement parsing, conceptual design, FEED/FDE, parametric design, and design decision-making; define R&D plans and evaluation metrics.

· Consolidate engineering parameters, BOM, simulation results, test data, technical documents, design rules, and historical project data into data structures for training, retrieval, prediction, and evaluation.

· Develop design-parameter recommendation, similar-solution retrieval, performance/risk prediction, multi-objective comparison, and generative-design assistance features.

· Incorporate constraints such as performance, cost, manufacturing conditions, delivery requirements, and engineering standards into models; analyze engineering reasonableness, explainability, and applicability boundaries.

· Drive requirement mapping, data modeling, algorithm experiments, and prototype validation around FEED/FDE; build reusable engineering knowledge and methods.

· Integrate data and functionality with the company’s industrial software, engineering data platform, CAD/CAE/PLM, or other systems as needed.

· Conduct experiments, algorithm evaluations, and scenario validation using real or authorized engineering cases; produce reproducible R&D records.

· Collaborate with engineering, algorithm, software, and product staff to turn business problems into R&D tasks, metrics, and milestone deliverables.

Requirements

· Bachelor’s degree or above in Mechanical, Mechatronics, Industrial Engineering, Automation, Energy/Power, Materials, Civil, Engineering Management, or another engineering discipline; AI, data science, or math composite background preferred.

· Understand the basic process of engineering design or projects; able to identify requirements, parameters, constraints, evaluation metrics, and trade-offs among alternatives.

· AI capability matching experience, e.g., machine learning, deep learning, data mining, predictive modeling, optimization, knowledge engineering, or generative AI; not required to cover all.

· Completed at least one AI or complex-data project with problem definition, data processing, model selection, evaluation metrics, and result validation; fintech experience may evidence method capability but must transfer to engineering design.

· Able to use Python or other suitable data-analysis/algorithm tooling for data processing, model experiments, and result analysis; willing to supplement FEED/FDE and engineering-software knowledge as needed.

· Cross-disciplinary communication and English technical reading ability; able to work with engineering, algorithm, and software staff to map problems and document model assumptions, data limitations, experiments, and conclusions.

Preferred Qualifications

· Experience in mechanical product design, engineering consulting, process design, project engineering, FEED/FDE, simulation analysis, or manufacturing digitalization.

· Familiar with LLMs, RAG, knowledge graphs, agents, time-series forecasting, operations research, or multi-objective optimization.

· Fintech, risk modeling, or complex business-decision-system experience with demonstrated transferability to engineering design.

· Knowledge of CAD/CAE/PLM, parametric design, generative design, topology optimization, or simulation data analysis.

· Used CATIA, SOLIDWORKS, NX, Creo, Abaqus, 3DEXPERIENCE, ZW3D, or related APIs; CAD/specific-software skill is a plus, not a baseline requirement.

· Experience with model serving, data platforms, MLOps, cloud deployment, or industrial software integration.

· Cross-disciplinary project experience across engineering, algorithm, and software; able to close the loop from problem definition to prototype validation.