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Mid-level role with niche MLOps, GPU, and Kubernetes skills reduces applicant density.
Specialized MLOps, GPU, and model-serving platform expertise limits transferability across non-AI industries.
Explicit 5–8 year requirement plus mandatory Kubernetes, GPU, cloud, and platform skills create strict filters.
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Design and build an enterprise-grade MLOps and AIOps platform on Kubernetes to deploy, manage, scale, and observe ML, DL, and generative AI workloads across cloud and on-prem environments.
Develop infrastructure and platform capabilities involving Kubernetes Operators, GPU-accelerated workloads, model-serving frameworks, and cloud-native technologies.
Collaborate cross-functionally to translate complex AI infrastructure requirements into secure, reliable, and user-friendly platform functionalities.
5–8 years in software engineering, platform engineering, DevOps, SRE, MLOps, or related infrastructure roles.
Strong hands-on experience with Kubernetes, including writing Operators and Custom Resource Definitions using Kubebuilder, Operator SDK, or similar.
Experience operating cloud-native infrastructure on AWS (EKS, EC2, S3, ECR, IAM, VPC, CloudWatch).
Experience deploying and running GPU-accelerated ML/DL/AI models in production environments.
Experienced in building and operating Kubernetes-native AI or MLOps platforms with strong ownership of technical initiatives.
Skilled in programming with Go or Python for building production APIs, controllers, or distributed backend services.
Familiar with GPU scheduling, model-serving frameworks (e.g., KServe, Triton, Ray Serve), and troubleshooting complex distributed AI infrastructure in hybrid-cloud or on-prem setups.