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Strong Tier-1 brand and mid-level AI role increase competition despite specialized agent-CAE expertise narrowing candidate pool.
Core AI skills are transferable but CAE simulation integration requires domain-specific engineering knowledge.
Explicit 2–5 years plus mandatory LLM, production, cloud, and integration skills raise screening rigidity.
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Design and deploy multi-agent AI systems to automate and orchestrate end-to-end engineering simulation workflows.
Integrate AI agents with CAE tools and external systems to build scalable, production-ready AI engineering solutions.
Develop and optimize retrieval-augmented generation (RAG) knowledge systems and ensure AI decision reliability, performance, and safety.
Bachelor’s or Master’s degree in Computer Science, AI, Data Science, or related field.
2–5 years of hands-on experience in AI/ML or applied AI engineering, including building end-to-end AI systems in production.
Strong Python programming skills and experience with LLMs (OpenAI or open-source models).
Experience with agent-based AI systems, API/microservices integration, and cloud platforms (preferably GCP).
Demonstrated experience in designing and deploying agentic AI systems with capabilities like planning, memory, and tool integration.
Comfortable working cross-functionally with CAE/mechanical engineers and software teams translating domain requirements into AI solutions.
Familiarity with advanced AI frameworks (LangChain, LangGraph, AutoGen, CrewAI), RAG architectures, and engineering simulation environments (e.g., ANSYS, Abaqus) is a strong plus.