• Applies technical knowledge and problem-solving methodologies to projects of moderate scope, with a focus on improving data and systems running at scale, and ensures end to end monitoring of 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 implications
• Drives the daily activities supporting the standard capacity process applications and partners with application and infrastructure teams to identify potential capacity risks and govern remediation statuses
• 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
• 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
• Ability to use (or learn) scripting languages such as Python, or other object oriented language like Ansible, C++, Java, etc.
• Experience working in a Linux environment.
• Expertise of networking fundamentals including: Network topology, VLANs, Routing, Switching, Global/Local Load Balancing and Firewalls.
• Expert in Multicast, low latency networks, and market data related technologies.
• Strong experience in Protocol Analyzers (Endace, Apcon, Wireshark, OpNet)
• Strong experience in capacity management tools (Grafana, SevOne)
• 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.
• Strong knowledge of one or more scripting languages

• Experience with multiple cloud technologies (ability to operate in and migrate across public and private clouds)