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Executive Aiops Engineer Jobs (NOW HIRING)

AI-First Data Platforms Lead - Executive Summary Location: Dallas TX-Onsite • Own the enterprise ... AIOps capabilities to proactively prevent outages and improve reliability. • Partner with ...

AI-First Data Platforms Lead - Executive Summary Location: Dallas TX-Onsite • Own the enterprise ... AIOps capabilities to proactively prevent outages and improve reliability. • Partner with ...

Orchestrate Agentic AI & AIOps Delivery: Serve as the technical delivery orchestrator for ... Harmonize diverse engineering, application support, data, and security teams. Manage resource ...

... AIOps is looking to find a Territory Account Manager to own the Polish market outright. What you ... Engage at the executive level. Build and maintain trusted relationships with CIOs, CTOs, Heads of ...

The Program Leader partners closely with architecture, engineering, and operations teams to ... A strong understanding of AI applications in IT operations (AIOps, automation, predictive analytics ...

We are growing rapidly and hiring top talent with leading AI skills across engineering, sales ... You will collaborate with senior FSI executives to deliver automated incident response and AIOps ...

Showing results 41-60

Executive Aiops Engineer information

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$42K

$94.9K

$155K

How much do executive aiops engineer jobs pay per year?

As of Sep 13, 2026, the average yearly pay for executive aiops engineer in the United States is $94,924.00, according to ZipRecruiter salary data. Most workers in this role earn between $80,000.00 and $108,000.00 per year, depending on experience, location, and employer.

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Cities with the most Executive Aiops Engineer job openings:

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What states have the most Executive Aiops Engineer jobs?

States with the most job openings for Executive Aiops Engineer jobs include:

AI Engineer

Dallas, TX • On-site

Stefanini
IT Services • 10K+ employees

Other

Posted 13 days ago


Job description

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