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Drive significant business impact through your capabilities and contributions, and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications.\nJob responsibilities\nRegularly provides technical guidance and direction to support the business and its technical teams, contractors, and vendors\nDrives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.\nApplies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to 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one or more areas of infrastructure engineering such as hardware, networking terminology, databases, storage engineering, deployment practices, integration, automation, scaling, resilience, or performance assessments\nDeep knowledge multiple infrastructure technologies and scripting languages (e.g., Python, JavaScript, yaml, Json, Bash, etc.)\nDeep knowledge of cloud infrastructure and multiple cloud technologies with the ability to operate in and migrate across public and private clouds\nKnowledge of on-premises and remote Data Center technologies and operations\nStrong experience in several OS and Hypervisor platforms (Hyper-V, ESXi, KVM, VMWARE, OpenShift)\nExperience in infrastructure automation, optimization and observability\nDemonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security\nStrong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching senior engineers/leads on compliant usage patterns and controls.\nExperience in deploying and using telemetry tools to improve visibility in service offerings\nSolid understanding of public cloud technologies, for AWS / Azure on Kubernetes, Terraform, experience in CI/CD and tools like Ansible, Terraform, Puppet for automating configuration management and deployment processes\nPreferred qualifications, capabilities, and skills\nWorking knowledge of 40/100/400G Ethernet and CWDM, DWDM and optical transport network technologies\nUnderstanding of different Optics and internals of a switch ASIC\nExperience configuring and troubleshooting routing and switching protocols (BGP, OSPF, VXLAN, MPLS, RSVP-TE, 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