大手グローバル保険会社での IT Support Managerの求人

Keep up continuous uptime and accessibility of critical business applications and services. This involves actively monitoring system performance, detecting potential issues, and implementing strategies to prevent downtime.
Respond to and resolve incidents and outages promptly. This includes troubleshooting problems, coordinating with other teams, and restoring service quickly.
Utilize AI and machine learning to analyze application logs and user behavior patterns, predicting potential issues and implementing proactive measures to prevent disruptions and performance degradation
Champion the adoption of AI technologies across the support organization. Provide training, create documentation, and establish best practices to upskill team members and foster a culture of AI innovation.
Automate repetitive, manual tasks (toil) to improve efficiency and reduce human error. This might involve scripting, developing tools, and improving infrastructure management processes.
Establish and maintain robust monitoring and alerting systems to gain real-time insights into system health and performance. This allows for proactive identification and detection of anomalies or potential issues.
After major incidents causing outages, conduct blameless post-mortem reviews to analyze the root causes of failures, document learnings, and implement corrective measures to prevent future occurrences.
Work with cross-functional teams, including product, engineering, and business stakeholders, to identify high-impact opportunities for AI integration. Clearly articulate how AI solutions will improve efficiency, enhance the customer experience, and deliver measurable business outcomes.
Establish clear, measurable targets for system performance and reliability, often based on Service Level Indicators (SLIs). These Service Level indicators and objectives guide development and operations priorities to maintain high levels of user satisfaction.
Stay informed on emerging AI technologies and industry trends. Evaluate and pilot new AI solutions to continuously enhance application support processes and capabilities.
Lead BAU projects and initiatives representing Japan application support team.

【Educational Qualification】
Bachelor’s degree in related field

【Specific Qualifications 】

Strong understanding of core concepts in AI and machine learning, including algorithms, model training, and deployment.
Proficiency in scripting languages (e.g., Python, Bash) and Infrastructure as Code (IaC) tools (e.g., Terraform, Ansible) is crucial.
A strong, engineering-minded approach to solving problems, with a focus on system improvement and long-term strategic impact.
Ability to quickly diagnose and resolve system incidents, minimize downtime, and implement solutions to prevent recurrence is paramount. This includes developing and adhering to incident response plans and conducting post-incident reviews (PIRs)
Ability to rely on data from metrics, logs, and other sources to understand system behavior, analyze performance, identify trends, and make informed decisions to improve system reliability.
Design, implement, and manage AI-driven tools, such as intelligent chatbots and autonomous support agents, to automate routine technical support tasks and empower end-users with self-service capabilities.
Excellent communication skills to articulate technical concepts, collaborate on projects, and foster a shared understanding of reliability goals. (Both in Japanese and English)
Proactive in learning new technologies, methodologies, and tools to adapt to changing environments and continuously improve their skills and the systems they manage.
Ability to handle multiple tasks, prioritize tasks and meet delivery deadlines.

【Total Experience】

3+ years of relevant technology experience, demonstrating progressive responsibility and leadership in overseeing regional technology teams.
5+ years team lead or project management experience
Demonstrated expertise in IT service management (ITSM) and observability tools, especially those that leverage AI for analytics
Hands-on experience with models like GPT, Claude, and their associated frameworks (e.g., RAG) is a key requirement
Practical experience running production systems, troubleshooting issues, and participating in on-call rotations is highly valued, building crucial intuition for real-world system failures.