Model Based Developer - Senior Expert

Eu Recruit
2 days ago

Role details

Contract type
Permanent contract
Employment type
Part-time (≤ 32 hours)
Working hours
Regular working hours
Languages
English
Experience level
Senior

Job location

Tech stack

Java
Artificial Intelligence
Application Integration Architecture
Code Generation
Program Optimization
Nvidia CUDA
Computer Programming
Eclipse Modeling Framework
Genetic Algorithm
Python
Performance Tuning
TensorFlow
Simulink
Stateflow
Systems Integration
UML
Reinforcement Learning
PyTorch
Large Language Models
Multi-Agent Systems
Prompt Engineering
Optimization Algorithms

Job description

  • Drive Technological Evolution: Lead the roadmap for proprietary MBD tools, moving toward AI-assisted, large-scale efficient simulation and optimized code generation.
  • Architect Systems: Design and develop the internal infrastructure for simulation and test generation within the energy and automation domains.
  • AI Integration: Research and implement multi-agent AI architectures (including LLMs, RAG, and orchestration) to enhance product capabilities.
  • Collaborative Innovation: Work across a multi-cultural team to turn complex research projects into functional, industry-leading solutions.

Requirements

  • Education: M.Sc. in Engineering (PhD strongly preferred).

  • Experience: 10+ years of expertise with commercial MBD tools used in PLC or energy domains (e.g., Siemens, Schneider Electric, Beckhoff, General Electric).

  • Domain Vision: Deep knowledge of MBD trends and the ability to drive innovation for energy-specific applications.

  • Model Engineering: Expertise in model-driven engineering, including model-to-text, text-to-model, and model-to-model transformations.

  • Code Generation: Mastery of code generation from models (e.g., XText, Acceleo, Model-IR) for embedded devices and optimization techniques.

  • Tooling & Simulation: Familiarity with UML, Simulink/Stateflow, and the Eclipse Modeling Framework (EMF), alongside experience in MIL/SIL simulation.

  • Optimization: Proven ability to solve complex problems using MILP, Simulated Annealing, or Genetic Algorithms.

  • Programming: Excellent Java and good Python skills.

  • AI Frameworks: Hands-on experience with TensorFlow, CUDA, PyTorch, LangChain, or Docling.

  • Advanced AI Implementation: Experience in prompt engineering, fine-tuning, reinforcement learning, model quantization, and building efficient multi-agent architectures.

  • Travel: Availability to travel within Europe and internationally (including Asia) for project collaboration, potentially for several weeks at a time.

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