Data Science/AI Developer__SA2121

• Investigate hybrid modelling approaches combining traditional simulation techniques (CFD, FEA, and multiphysics) with AI/ML.
  
• Explore and develop AI agents integrated with simulation tools such as Simcenter STAR-CCM+ and Simcenter HyperMesh.
  
• Design and evaluate multi-agent systems for orchestrating complex engineering tasks and supporting decision-making.
  
• Prototype, benchmark, and validate emerging AI approaches against real-world industrial engineering problems.
  
• Develop agentic AI workflows involving agent design, tool usage, and orchestration.
  
• Collaborate with simulation experts, software developers, and AI specialists across business teams.
  
• Prepare and present technical reports, presentations, and internal demonstrations for engineering and leadership audiences.
  

Requirements

• 3+ years of experience in Data Science, Machine Learning, Applied Mathematics, or a closely related field.
  
• Strong hands-on programming experience in Python.
  
• Practical experience building solutions using Large Language Models (LLMs) and agentic AI frameworks, including agent design, tool use, and orchestration beyond basic API integration.
  
• Strong understanding of machine learning fundamentals and the ability to critically evaluate AI solutions for effectiveness, reliability, and practical applicability.
  
• Excellent analytical, problem-solving, and experimentation skills.
  
• Ability to work independently in ambiguous, exploratory environments with limited specifications.
  
• Strong written and verbal communication skills, with the ability to explain technical concepts and present findings to engineering teams and leadership.
  

Good-to-Have Skills:

• Exposure to engineering simulation techniques such as CFD, FEA, and multiphysics, including meshing, solving, and post-processing workflows.
  
• Familiarity with Simcenter STAR-CCM+, Simcenter HyperMesh, or comparable commercial simulation tools.
  
• Knowledge of knowledge graphs, multi-agent orchestration, and AI-driven engineering systems.
  
• Experience with workflow automation tools, MLOps practices, or experiment-tracking platforms.