Principal Messaging Systems Engineer - AI-Driven Ops

Boehringer Ingelheim

Muntinlupa

On-site

PHP 1,800,000 - 2,400,000

Full time

5 days ago

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Job summary

Boehringer Ingelheim’s Global Collaboration and Event Technology – Messaging team is seeking a Principal Systems Engineer to lead the delivery and support of secure, scalable messaging solutions and to drive AI-enabled operations across a global environment.

You will design and optimize Exchange Online, Defender, mail routing and DNS configurations while implementing automation and AI-assisted incident triage to improve reliability and user experience worldwide.

Qualifications

Experience supporting enterprise messaging in a complex, globally distributed environment.
Ability to perform System Lead, System Lead Delegate or Subject Matter Expert responsibilities.
Understanding of data management, generative AI, AI agents or intelligent operations, with the ability to translate operational needs into practical use cases.
Strong analytical, troubleshooting, documentation, communication and stakeholder-management skills.

Responsibilities

Provide expert-level administration and support for Exchange Online and Microsoft Defender.
Design, manage, and optimize enterprise email architecture across cloud-based, hybrid, and on-premises environments.
Manage mail routing, transport, message delivery, connectors, gateways and SMTP relay infrastructure.
Manage messaging-related DNS and domain configurations, including email authentication through SPF, DKIM, DMARC, MTA-STS, and TLS reporting.
Secure mail transport and manage message encryption, anti-phishing, anti-spoofing, threat protection, and incident response.
Manage shared and functional mailboxes, distribution groups, dynamic distribution groups, and mail-enabled directory objects.
Support client connectivity, mobile messaging, calendaring, critical notifications, and communication integrations.
Monitoring, message tracing, service health, capacity, performance, reporting and operational analytics.
Use operational data, dashboards and service-health indicators to improve reliability, transparency and decision making.
Identify repetitive work and implement controlled automation, orchestration and self-healing operational workflows.
Develop practical AI-assisted capabilities for incident triage, knowledge retrieval, troubleshooting, anomaly identification, root cause analysis and operational reporting.
Measure outcomes such as reduced manual effort, faster resolution, improved service stability and better support experience.