AI Engineer

AI-First Data Platforms Lead - Executive Summary

Location: Dallas TX-Onsite

• Own the enterprise database platform strategy, architecture, governance, and technology roadmap.

• Lead the transformation from traditional DBA operations to an AI-first Database Platform Engineering model.

• Drive AI-powered automation for database provisioning, monitoring, maintenance, performance tuning, and incident management.

• Build and manage self-service database provisioning capabilities to accelerate engineering delivery and reduce manual effort.

• Ensure database platforms are secure, scalable, resilient, highly available, and cost-efficient across on-premises and cloud environments.

• Lead database modernization, consolidation, migration, and cloud adoption initiatives.

• Establish standards, best practices, governance, and lifecycle management for enterprise database platforms.

• Implement observability, predictive monitoring, and AIOps capabilities to proactively prevent outages and improve reliability.

• Partner with Engineering, Infrastructure, Security, Architecture, and Application teams to deliver platform services and approved patterns.

• Drive adoption of Infrastructure-as-Code (IaC), DevOps, CI/CD, and Database-as-a-Service (DBaaS) capabilities.

• Ensure compliance, data protection, access controls, backup, recovery, and disaster recovery readiness.

• Mentor and develop database engineers while fostering a culture of automation, innovation, and operational excellence.

• Evaluate emerging database, AI, and cloud technologies to continuously improve platform capabilities.

• Optimize platform costs through standardization, automation, capacity planning, and resource utilization.

Business Impact

* Reduces operational risk through intelligent automation and standardized platforms.

* Improves performance, availability, reliability, and security of enterprise databases.

* Accelerates provisioning from days to minutes through self-service capabilities.

* Enhances compliance and governance while reducing manual administrative effort.

* Lowers long-term support and infrastructure costs through automation and platform rationalization.

* Enables engineering teams to move faster with AI-enabled platform services and expert guidance.

* Creates a scalable foundation that supports enterprise growth, cloud strategy, and future AI initiatives.

Key Success Measures

* Significant reduction in manual DBA effort through AI and automation.

* Faster database provisioning and deployment cycles.

* Improved uptime, reliability, and recovery capabilities.

* Reduced incident volume and Mean Time to Resolution (MTTR).

* Increased adoption of self-service database services.

* Lower total cost of ownership (TCO) through optimization and standardization