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Tier-1 brand, mid-level generalist title, and metro location increase applicant competition.
Core ML serving, cloud, and Kubernetes skills transfer across industries, but domain ML experience is beneficial.
Explicit 5+ years requirement plus mandatory ML, cloud, Kubernetes, and CI/CD experience tightens screening.
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Design and implement scalable AI/ML model serving systems with low latency across hybrid cloud environments.
Architect and manage hybrid cloud infrastructure integrating on-premises and major cloud providers (AWS, Azure, GCP) for optimal performance and cost.
Lead deployment, versioning, performance monitoring, and compliance for AI/ML models, ensuring reliability and security in production.
Bachelor's or Master's degree in Computer Science or equivalent practical experience.
5+ years of software development and engineering experience with production system delivery.
Hands-on experience with AI/ML frameworks (e.g., TensorFlow, PyTorch) and lifecycle of AI/ML models from training to deployment.
Experience managing hybrid cloud architectures and proficiency in Python; familiarity with containerization (Docker) and orchestration (Kubernetes).
Experienced in architecting and operating AI/ML serving platforms within hybrid cloud environments involving multiple cloud providers.
Strong background in software engineering with deep knowledge of DevOps practices, CI/CD pipelines, and system performance optimization.
Capable of collaborating with cross-functional AI/ML and data engineering teams to translate models into production-ready scalable systems.