August 31
🔄 Hybrid – San Francisco
• Lead Cloud-Infrastructure components with high-availability, scalability, and reliability mindset. • Advocate engineering best practices, code reviews, and automated unit/end-to-end testing. • Optimize all cloud-based infrastructure environments to control overall costs. • Define Service-Level Objectives (SLO) and track Service-Level Indicators (SLI). • Automate deployment of cloud-infrastructure components using Infrastructure-as-Code to deliver services at high velocity. • Design and implement a good observability and alerting framework. • Plan and build networking topologies for secure communication. • Monitor and resolve vulnerabilities and threats in source-code and cloud infrastructure. • Set up policies for secure access to production environment.
• Track record of building scalable and multi-region (for high-availability) infrastructure components on public cloud-based platforms like Google Cloud Platform (GCP). • Comfortability with programming languages like Python, GoLang, and scripting languages like bash. • Comfortability with orchestration platforms like Kubernetes and Airflow. • Familiarity with Infrastructure-as-Code technologies like terraform. • Awareness of cloud-networking technologies like Virtual Private Cloud (VPC), subnets, firewall rules, network routing rules, network peering. • Proficiency with public cloud concepts and delivering working solutions on public cloud infrastructure, preferably GCP (BigQuery, BigTable, Pub/Sub, SecretManager). • Strong software engineering skills, and experience writing clear, concise, and maintainable code considering design principles and applying sound testing practices. • Systematic and goal-directed approach to project management; are comfortable dealing with ambiguity and ruthlessly prioritizing and managing your time with a sense of urgency.
• Relyance AI is an equal-opportunity employer. • We celebrate representation and are committed to creating an inclusive environment for all employees. • Fair and equitable compensation practices.
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