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Strong funded brand and metro location, but senior, niche ML-infra specialization limits candidate pool.
High because specialized ML infrastructure, GPU inference, and enterprise compliance require domain-specific experience.
High due to explicit 10–15 years, principal-level requirement, and many mandatory ML-infra and compliance skills.
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Design and oversee scalable, low-latency distributed systems for voice and chat AI with enterprise-grade security and compliance.
Ensure reliability and optimize GPU/TPU utilization for cost-effective AI training and inference in multi-cloud environments.
Lead technical architecture and mentor engineering teams to translate AI research into robust production platforms.
10-15 years in large-scale systems architecture with at least 5 years at principal architect level.
Expertise in distributed systems, cloud-native architectures, Kubernetes, containerization, and microservices.
Experience with scalable ML infrastructure, including model serving and GPU/accelerator utilization.
Experience in conversational AI or real-time inference workloads; familiarity with MLOps platforms and inference optimization frameworks.
Experienced technical leader skilled at architecting and scaling multi-cloud, real-time conversational AI systems with enterprise reliability.
Strong background in performance engineering, observability, and embedding security-by-design practices in AI/ML workloads.
Proven capability to bridge advanced AI research and practical deployment through hands-on mentorship and adoption of cutting-edge infrastructure technologies.