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