Design and deploy multi-agent AI systems that autonomously orchestrate engineering simulation workflows end-to-end
Build LLM-powered agents with tool use, memory, planning, and self-correction capabilities integrated into production CAE settings
Develop agent orchestration pipelines using frameworks such as LangGraph, AutoGen, CrewAI, or custom agentic architectures
Integrate agents with external tools and APIs: simulation solvers, mesh processing tools, databases, and internal engineering systems
Implement Retrieval-Augmented Generation (RAG) systems grounded in engineering domain knowledge, simulation data, and technical documentation
Collaborate with mechanical and simulation engineers to translate complex domain requirements into agentic AI solutions
Monitor, debug, and optimize agent performance, latency, and cost in production systems

Skill Set:
AI Agentic Engineer - Generative AI, LLM, RAG, Model Fine-tuning, Deep-Learning,



Transformers

Domain Knowledge:
Agentic frameworks: LangChain, LangGraph, AutoGen, CrewAI, Semantic Kernel, or similar
RAG architecture: vector databases (Pinecone, ChromaDB), chunking strategies, embedding models
MLOps and deployment: Docker, CI/CD pipelines, model serving (FastAPI, vLLM, TGI)
Structured output and function calling: JSON schema enforcement, tool registration, agent memory patterns
CAE/FEA software awareness (LS-DYNA, Abaqus, ANSYS) a plus, not required

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