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Tier-1 brand, mid-level (5+ years), metro location, and broad skillset expectations increase competition.
ML platform and hybrid cloud skills transfer across industries but require specialized ML and infrastructure experience.
Explicit 5+ years plus mandatory ML, cloud, Kubernetes, and deployment experience makes screening highly selective.
Design and implement scalable AI/ML model serving systems ensuring low latency and high efficiency across hybrid cloud environments.
Architect and manage hybrid cloud infrastructure integrating on-premises and public cloud platforms (e.g., AWS, Azure, GCP) optimizing for performance and cost.
Oversee AI/ML model deployment with CI/CD pipelines, handle versioning, monitor production performance, and ensure compliance with security standards.
5+ years of software development experience delivering production systems.
BS or MS in Computer Science or equivalent practical experience.
Hands-on experience with AI/ML frameworks (TensorFlow, PyTorch) and Python programming.
Experience managing hybrid cloud architectures and knowledge of containerization (Docker) and orchestration (Kubernetes).
Engineer experienced in production-level AI/ML platform development with hybrid cloud environment management.
Familiar with DevOps best practices including CI/CD pipelines and infrastructure as code for smooth model deployments.
Demonstrated ability to optimize system performance, monitor AI/ML model health, and maintain security compliance in complex distributed systems.