Track Lead Microsoft Azure IaaS, Azure Kubernetes Service

Job description

 Basic working knowledge of Data Center, IT Infrastructure and Operations concepts.  Minimum 2 years of experience (including internships/projects) in IT Automation, scripting, or infrastructure support, with an interest in Automation and AI.  Willingness to work as part of a cross-functional team, taking direction well and collaborating effectively with peers.  Basic understanding of ITIL and DevOps concepts; willing to build this further on the job.  Foundational exposure to cloud automation tools and on-premise infrastructure solutions, gained through coursework, certifications, internships, or personal projects.  Basic infrastructure knowledge across Operating System, Compute, Storage, Backup, and Network components, on-prem and on Cloud, with an eagerness to develop automation for these areas.  Awareness of Cognitive Services, Machine Learning concepts, and GenAI frameworks; keen to build hands-on exposure.  Basic exposure to continuous integration and continuous deployment (CI/CD) pipelines through academic or self-learning projects.  Basic awareness of security considerations in automated workflows, with willingness to learn assessment and mitigation practices.  Industry certifications related to cloud technologies, automation, or AI (e.g., AWS or Azure fundamentals) are an added advantage.  Familiarity with MLOps concepts (e.g., model monitoring, CI/CD for ML pipelines) is a plus.  Working knowledge of, or willingness to learn, infrastructure-as-code and configuration management tools such as Ansible and Terraform.  Basic exposure to DevOps toolchains (e.g., Git, Jenkins, Azure DevOps) for build, release, and deployment automation.  Awareness of observability and monitoring tools such as Dynatrace for infrastructure and application health monitoring.  Interest in learning how to build or integrate with Model Context Protocol (MCP) servers to connect automation/AI workflows with enterprise tools.  Basic knowledge of ServiceNow (SNOW) modules (Incident, Change, Request) and interest in automation/API integration with ITSM platforms is a plus.  Exposure to integrating automation scripts with enterprise systems, gained through coursework or projects  Good programming/scripting skills (e.g., Python, Java, JavaScript).  Basic knowledge of APIs and data integration concepts (REST, etc).  Basic awareness of AI/automation risks, data privacy, and regulatory compliance concepts (e.g., GDPR, HIPAA).  Ability to manage assigned tasks and priorities, and to raise escalations promptly to seniors when needed.  Good problem-solving skills and willingness to learn how to navigate complex and ambiguous scenarios.  Basic understanding of vendor coordination (support role, not lead)  Agile mindset and eagerness to learn and deliver results.  Developing knowledge of the customer, market, and competitors.  Living the four Allianz People Attributes: Customer & Market Excellence, Collaborative Leadership, Entrepreneurship and Trust Key Responsibilities  Basic working knowledge of Data Center, IT Infrastructure and Operations concepts.  Minimum 2 years of experience (including internships/projects) in IT Automation, scripting, or infrastructure support, with an interest in Automation and AI.  Willingness to work as part of a cross-functional team, taking direction well and collaborating effectively with peers.  Basic understanding of ITIL and DevOps concepts; willing to build this further on the job.  Foundational exposure to cloud automation tools and on-premise infrastructure solutions, gained through coursework, certifications, internships, or personal projects.  Basic infrastructure knowledge across Operating System, Compute, Storage, Backup, and Network components, on-prem and on Cloud, with an eagerness to develop automation for these areas.  Awareness of Cognitive Services, Machine Learning concepts, and GenAI frameworks; keen to build hands-on exposure.  Basic exposure to continuous integration and continuous deployment (CI/CD) pipelines through academic or self-learning projects.  Basic awareness of security considerations in automated workflows, with willingness to learn assessment and mitigation practices.  Industry certifications related to cloud technologies, automation, or AI (e.g., AWS or Azure fundamentals) are an added advantage.  Familiarity with MLOps concepts (e.g., model monitoring, CI/CD for ML pipelines) is a plus.  Working knowledge of, or willingness to learn, infrastructure-as-code and configuration management tools such as Ansible and Terraform.  Basic exposure to DevOps toolchains (e.g., Git, Jenkins, Azure DevOps) for build, release, and deployment automation.  Awareness of observability and monitoring tools such as Dynatrace for infrastructure and application health monitoring.  Interest in learning how to build or integrate with Model Context Protocol (MCP) servers to connect automation/AI workflows with enterprise tools.  Basic knowledge of ServiceNow (SNOW) modules (Incident, Change, Request) and interest in automation/API integration with ITSM platforms is a plus.  Exposure to integrating automation scripts with enterprise systems, gained through coursework or projects  Good programming/scripting skills (e.g., Python, Java, JavaScript).  Basic knowledge of APIs and data integration concepts (REST, etc).  Basic awareness of AI/automation risks, data privacy, and regulatory compliance concepts (e.g., GDPR, HIPAA).  Ability to manage assigned tasks and priorities, and to raise escalations promptly to seniors when needed.  Good problem-solving skills and willingness to learn how to navigate complex and ambiguous scenarios.  Basic understanding of vendor coordination (support role, not lead)  Agile mindset and eagerness to learn and deliver results.  Developing knowledge of the customer, market, and competitors.  Living the four Allianz People Attributes: Customer & Market Excellence, Collaborative Leadership, Entrepreneurship and Trust Skill Requirements  Basic working knowledge of Data Center, IT Infrastructure and Operations concepts.  Minimum 2 years of experience (including internships/projects) in IT Automation, scripting, or infrastructure support, with an interest in Automation and AI.  Willingness to work as part of a cross-functional team, taking direction well and collaborating effectively with peers.  Basic understanding of ITIL and DevOps concepts; willing to build this further on the job.  Foundational exposure to cloud automation tools and on-premise infrastructure solutions, gained through coursework, certifications, internships, or personal projects.  Basic infrastructure knowledge across Operating System, Compute, Storage, Backup, and Network components, on-prem and on Cloud, with an eagerness to develop automation for these areas.  Awareness of Cognitive Services, Machine Learning concepts, and GenAI frameworks; keen to build hands-on exposure.  Basic exposure to continuous integration and continuous deployment (CI/CD) pipelines through academic or self-learning projects.  Basic awareness of security considerations in automated workflows, with willingness to learn assessment and mitigation practices.  Industry certifications related to cloud technologies, automation, or AI (e.g., AWS or Azure fundamentals) are an added advantage.  Familiarity with MLOps concepts (e.g., model monitoring, CI/CD for ML pipelines) is a plus.  Working knowledge of, or willingness to learn, infrastructure-as-code and configuration management tools such as Ansible and Terraform.  Basic exposure to DevOps toolchains (e.g., Git, Jenkins, Azure DevOps) for build, release, and deployment automation.  Awareness of observability and monitoring tools such as Dynatrace for infrastructure and application health monitoring.  Interest in learning how to build or integrate with Model Context Protocol (MCP) servers to connect automation/AI workflows with enterprise tools.  Basic knowledg