Back
Tier-1 employer plus mid-level role and general platform title increases candidate competition.
Specialized AI platform and GPU/Kubernetes skills are somewhat transferable but favor platform/cloud engineers.
Explicit 5+ years plus mandatory GKE, GPU, LLM-serving, and specific cloud tooling make filters strict.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Implement, maintain, and optimize AI platform capabilities on Google Kubernetes Engine (GKE) in Google Cloud Platform (GCP) to support Generative AI applications and self-hosted models.
Develop and integrate platform components including custom Kubernetes resources, telemetry pipelines, and reusable platform templates to improve reliability and developer experience.
Lead execution of technical roadmaps aimed at enhancing platform reliability, reducing operational overhead, and accelerating AI feature delivery cycles.
5+ years of relevant experience in platform or cloud-native engineering.
Hands-on experience with deploying and scaling self-hosted Large Language Models (LLMs) on GKE using inference engines like vLLM, NVIDIA NIM, or SGLang.
Strong proficiency in Python or Go programming languages for automation and custom tooling.
Practical experience with Google Kubernetes Engine (GKE), GCP infrastructure, and service mesh technologies such as Istio Ambient Mode.
Experienced with AI infrastructure for Generative AI, including deployment, observability (OpenTelemetry), and agentic workflow tools integration.
Strong background in cloud-native networking and container orchestration technologies, particularly within GCP environment.
Demonstrates advanced software engineering practices with emphasis on maintainability, testing, and clear technical documentation.