More jobs:
AI Engineer
Job in
Dallas, Dallas County, Texas, 75215, USA
Listed on 2026-09-01
Listing for:
Stefanini
Full Time
position Listed on 2026-09-01
Job specializations:
-
IT/Tech
Cloud Computing: Infrastructure & Operations, SRE/Site Reliability, Data Engineering, Systems Engineer
Job Description & How to Apply Below
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), Dev Ops, 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
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