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Metro location and popular AI title increase competition, but specialized NVIDIA/GenAI requirements reduce candidate pool.
Industrial digital twin and NVIDIA/Omniverse specialization limits cross-industry transferability.
Multiple mandatory seniority, narrow tech stack, onsite requirement, and no visa sponsorship increase screening strictness.
Lead design and development of generative and optimization AI models for manufacturing process simulation, scheduling, and predictive analytics.
Orchestrate end-to-end AI pipelines including synthetic data generation, model training, validation, and deployment with real-time industrial data.
Mentor teams on best practices for simulation modeling, AI stacks, and deployment workflows ensuring scalable, low-latency industrial AI solutions.
7-10 years experience with Bachelor’s degree; or 4-6 years with Master’s; or 1-3 years with PhD in CS, AI, Data Science, Mathematics, or related quantitative field.
Strong experience with Generative AI models, Amazon Bedrock, AWS AI/ML services, and building Agentic AI systems using frameworks like LangChain or AutoGen.
Expertise in cloud-native architecture, LLMOps, MLOps, and Gen AI security, cost, and governance in cloud environments.
Onsite work required five days a week; Visa sponsorship not available.
Experienced AI engineer with a strong background in industrial manufacturing AI applications focusing on process optimization and real-time data integration.
Proficient in operationalizing AI models at scale including CI/CD pipelines, containerization, and deployment on cutting-edge AI platforms like NVIDIA's stack.
Demonstrated leadership in mentoring teams, guiding architectural AI solutions, and bridging AI innovation with manufacturing process improvements.