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Strong employer brand, mid-level (2–5 years) AI role, metro location and popular LLM skillset amplify competition.
Core ML/LLM skills are transferable but CAE/simulation integration needs domain exposure, reducing portability.
Explicit 2–5 years requirement plus mandatory production LLM, Python, and integration skills make filters strict.
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Design and deploy multi-agent AI systems integrating with CAE environments to automate simulation workflows end-to-end.
Develop and optimize Retrieval-Augmented Generation pipelines and vector search systems using simulation data and technical documentation.
Collaborate cross-functionally with simulation engineers, software teams, and data scientists to deliver production-ready AI systems with monitored performance and reliability.
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 with end-to-end AI system development experience.
Strong Python programming skills and experience with LLMs and agent-based AI systems.
Experience with APIs, system integration, cloud platforms (preferably GCP), and software engineering best practices.
Experienced in building production-grade AI agents with capabilities such as planning, memory, and tool integration, in complex engineering contexts.
Comfortable working at the intersection of AI, simulation engineering, and data platform integration with cross-functional teams.
Skilled in reliability engineering for AI, including designing evaluation frameworks and implementing guardrails to mitigate failures and hallucinations.