Back
Strong employer brand and Bangalore metro increase applicant interest, but niche platform and agentic AI skills limit competition.
Specialized platform, GCP, Kubernetes, and agentic AI requirements reduce cross-industry transferability.
Multiple mandatory technologies, GCP requirement, and explicit 6–10 years experience enforce strict shortlisting.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Architect and build enterprise-scale AI-native platforms on Google Cloud that support data pipelines, GenAI applications, and multi-agent AI systems.
Design, implement, and operate scalable, automated, and observable AI platforms involving agent lifecycle management, multi-agent orchestration, and AI-driven automation.
Collaborate with AI/ML engineers, SRE, and product teams to integrate platform services, enable automation-first workflows, and support platform governance and data architecture.
6–10 years of experience in Platform Engineering, Data Engineering, Cloud Architecture, or AI Platform Engineering.
Strong experience with Google Cloud Platform (GCP) and cloud-native platform engineering including Kubernetes, Terraform, and CI/CD automation.
Proven skills in programming with Java and Python, building microservices, real-time data pipelines, and distributed systems.
Experience with multi-agent AI frameworks, LLM integration, observability tools (Prometheus, Grafana, OpenTelemetry), and data governance platforms.
Experienced with designing and operating advanced AI and agentic platforms integrating multi-agent orchestration and GenAI at enterprise scale.
Comfortable leading technical architecture and collaboration across cross-functional teams including AI/ML, SRE, and product groups in cloud-first environments.
Skilled in implementing end-to-end observability, automation-first operations, and scalable, governed data architectures suited for high-complexity AI workloads.