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Specialized platform+Agentic AI skills, metro location, and strong employer brand create moderate applicant competition.
Role requires deep cloud, platform, and AI/agentic system expertise, making cross-industry transferability limited.
Explicit 6–10 years requirement, mandatory GCP and multiple specific platform technologies make filters strict.
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Architect and build cloud-native AI and data platforms on Google Cloud supporting multi-agent orchestration, real-time data pipelines, APIs, and GenAI applications.
Design and implement agentic AI systems including lifecycle management, multi-agent communication protocols, and AI-driven platform automation.
Develop observability frameworks, data governance, automation-first workflows, and enable engineering self-service capabilities across the AI platform ecosystem.
6–10 years of experience in Platform Engineering, Data Engineering, Cloud Architecture, or AI Platform Engineering.
Strong experience with Google Cloud Platform (GCP), Kubernetes, Terraform, and Infrastructure-as-Code.
Proficiency in Java, Python, microservices architecture, streaming data systems (e.g., Kafka, Pub/Sub), and agentic AI frameworks (CrewAI, LangGraph, AutoGen).
Work Experience Required: 6–10 years in relevant platform or AI engineering roles.
Experienced in building scalable, enterprise-grade AI-native platforms and multi-agent AI systems at scale.
Strong cloud-native and distributed system design skills with ability to lead cross-functional collaboration among AI/ML, SRE, and product teams.
Deep expertise in observability, data governance, and automation of AI-driven platform operations enabling reliability and developer productivity.