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High: Tier-1 brand, metro location, mid-level AI role with broad LLM/production skill requirements.
Medium because core ML engineering skills are transferable, but CAE simulation integration increases domain specificity.
High due to explicit years, mandatory AI/ML production experience, and specific LLM and engineering skill requirements.
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Design and deploy multi-agent AI systems to automate and orchestrate end-to-end simulation engineering workflows.
Integrate AI agents with CAE simulation tools, external APIs, and data platforms for production-grade engineering environments.
Develop and optimize Retrieval-Augmented Generation (RAG) pipelines, ensure AI system performance, reliability, and safety with evaluation and guardrail implementations.
Bachelor's or Master's degree in Computer Science, AI, Data Science, or related field.
2–5 years of hands-on experience in applied AI engineering or AI/ML, including building production end-to-end AI systems.
Strong Python programming skills and experience with LLMs (OpenAI or open-source).
Experience with agent-based systems, API/microservice integration, and familiarity with cloud platforms (preferably GCP).
Experienced in agentic AI system design focusing on planning, memory, and orchestration within engineering or simulation domains.
Proficient in integrating AI agents with complex engineering software environments and cross-functional collaboration with simulation and mechanical engineers.
Skilled in development and deployment of RAG systems, AI safety practices, and monitoring AI system performance for reliability and failure mitigation.