• Applies technical knowledge and problem-solving methodologies to projects of moderate scope, with a focus on improving the data and systems running at scale, and ensures end to end monitoring of infrastructure and applications
• Uses enterprise-authorized AI capabilities within the work environment to accelerate monitoring and capacity analysis and documentation, validating outputs and handling operational data according to sensitivity and security requirements
• Resolves most nuances and determines appropriate escalation path
• Executes conventional approaches to build or break down technical problems while considering upstream and downstream data and systems or technical applications
• Drives the daily activities supporting the standard capacity process applications
• Partners with application and infrastructure teams to identify potential capacity risks and govern remediation statuses
• Be accountable for making significant decisions for a project consisting of multiple technologies and applications
• Applies reuse-first, AI-assisted approaches to identify recurring capacity risks and improve remediation workflows, ensuring changes are validated and aligned to resiliency and security expectations

Required qualifications, capabilities, and skills

• Formal training or certification on infrastructure engineering concepts and 3+ years applied experience
• 2+ years of network operations experience
• Operating knowledge of one or more areas of infrastructure engineering such as software defined networks / zero touch provisioning, SD-WAN, fabric switching, Wi-Fi, and network security technologies and practices.
• Proficient in specific network infrastructure technology and programming languages (e.g., Python, Ansible, etc.)
• Strong knowledge of one or more infrastructure disciplines such as hardware, networking terminology, databases, storage engineering, deployment practices, integration, automation, scaling, resilience, and performance assessments
• Demonstrated experience using enterprise-authorized AI capabilities within the work environment to support infrastructure engineering workflows with strong validation habits and awareness of data sensitivity.
• Ability to review and validate AI-assisted recommendations before implementation, escalating when uncertain and ensuring outcomes align to resiliency, security, and auditability expectations.
• Knowledge of cloud infrastructure and multiple cloud technologies with the ability to operate in and migrate across public and private clouds
Drives to develop infrastructure engineering knowledge of additional domains, data fluency, and automation knowledge