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Metro Bangalore and popular SRE title increase applicant density, but seniority and niche cloud/data platform skills moderate competition.
Deep GCP, data/AI platform and agentic AI SRE expertise required reduces cross-industry transferability.
Explicit 9+ years, Staff-level, mandatory GCP, IaC and observability requirements make shortlisting highly strict.
Own reliability, scalability, and operability of enterprise data and AI platforms on GCP and Azure, applying SRE principles including SLOs, SLIs, error budgets, and incident management.
Lead automation and toil reduction by building self-serve infrastructure, automating deployment pipelines using Infrastructure-as-Code (Terraform, Helm, GitOps).
Architect and optimize multi-cloud platforms (GCP and Azure), ensuring security, cost efficiency, and high availability for data and AI workloads including agentic AI systems.
Master's degree in Computer Science, Engineering, or related field or equivalent experience.
9+ years of experience in Site Reliability Engineering, DevOps, or Platform Engineering in large-scale production environments.
Deep hands-on expertise with Google Cloud Platform services (GKE, Cloud Run, BigQuery, Pub/Sub, GCS, Composer, Dataflow, Vertex AI) and Infrastructure-as-Code tools (Terraform, Helm).
Experience with observability tooling, multi-cloud (GCP and Azure) environments, and strong understanding of data security practices (IAM, encryption, secrets management).
Experienced in leading reliability engineering at organizational scale with a focus on eliminating toil and establishing SRE disciplines across teams.
Skilled in multi-cloud architecture and operations, integrating GCP and Azure, with an emphasis on security, cost governance, and operational consistency.
Practitioner of SRE best practices with experience in large-scale data and AI platform environments, including agentic AI and modern observability tooling.