Senior Software Developer- AI Enablement

Developer

Senior Software Developer- AI Enablement

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  • Date posted
    September 30, 2026
  • Expiration date
    December 30, 2026
  • Application ends
    December 30, 2026

As a Senior Software Developer , you will build the software that powers  virtual development environments, SIL workflows, and AI-enhanced simulation capabilities. You bring strengths in automation, embedded systems, and problem-solving – and this role expands your exposure to cloud, data, ML fundamentals, and large-scale simulation architecture.
What You’ll Do (Responsibilities):

  • Develop backend services supporting virtual , simulation orchestration, and model execution.
  • Build tools for SIL workflows including scenario execution, data capture, and automation.
  • Integrate AI/ML components into simulation or validation pipelines.
  • Design APIs for simulation control, artifact management, and orchestration.
  • Optimize performance for compute-intensive workloads.
  • Collaborate with DevOps and simulation teams to ensure seamless integration.
  • Contribute to CI/CD workflows for simulation and AI components.

Your Skills & Abilities (Required Qualifications):

  • Bachelor’s degree (or higher) in Engineering, Computer Science, or related field.
  • 7+ years of relevant experience in software development, simulation, or embedded systems.
  • Strong programming skills in Python, C++, C#, or Java.
  • Experience with simulation or virtualization (vECUs, FMUs, SIL).
  • Understanding of cloud services and distributed systems.
  • Experience with CLI-based architecture for tools design.
  • Knowledge of MCP-based architecture for AI tools design.
  • Experience with databases for simulation metadata and results.

What Will Give You a Competitive Edge (Preferred Qualifications):

  • Optional AI skills: ML lifecycle basics, Vector search or embeddings, and model integration.
  • Experience with microservices for simulation orchestration.
  • Knowledge of Kubernetes for running compute workloads.
  • Performance tuning expertise for simulation or AI pipelines.
  • Experience with automotive data and domain modeling.
  • Experience with ontology-based engineering processes and architecture frameworks.
  • Advanced knowledge of ontology technologies and ontology design patterns (Owl, RDF, SPARQL, Automated reasoning).
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