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AI Engineer

Stefanini IT Solutions

 

Dallas, TX, USA

Posted On: Just posted
Experience: 5+ years
Availability: Hybrid
Openings: 1
Category: AI Engineer
Tenure: No Preference/Any
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Description

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.

 

Job Requirements

Details:

 

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

Education

Any Graduate

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