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Tier-1 brand, mid-level generalist title, metro hiring and popular skills drive high competition.
Specialized Kubernetes and ML platform experience limits transferability moderately across industries.
Explicit 5–7 years and many mandatory platform, Kubernetes, and ML infrastructure skills make screening strict.
Architect and develop scalable Kubernetes-based AI/ML platform infrastructure supporting LLMs and GPU workloads.
Lead platform reliability, scalability, automation (IaC), observability, and operational excellence including on-call responsibilities.
Mentor engineers and drive adoption of modern technologies and processes in a fast-paced AI/ML environment.
5-7 years of software engineering experience.
Strong expertise in Golang or Python and hands-on Kubernetes platform experience.
Experience with ML frameworks such as TensorFlow or PyTorch and production ML system deployment including CI/CD pipelines.
Proficiency in cloud platforms, distributed systems orchestration, and infrastructure as code automation.
Experienced in designing and operating production-grade Kubernetes clusters with ML workflows and GPU support.
Demonstrates leadership in technical direction, mentoring, and driving platform improvements and reliability.
Skilled in developing scalable distributed systems, integrating ML tools like Kubeflow, KServe, Airflow, and optimizing AI workflows and CI/CD processes.