Senior AI-Driven Database Engineer

Namely

Mountain View (CA)

On-site

USD 140,000 - 190,000

Full time

3 days ago
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Job summary

Namely is seeking an expert-level Database Operations Engineer to administer and support Cornerstone’s database infrastructure using AI and intelligent automation. You will ensure high availability, security, and performance across production and non-production environments supporting critical apps and automation platforms.

You will drive AI-powered monitoring, automated remediation, and autonomous diagnostics while collaborating on LLM-based database agents, knowledge bases, and incident

Qualifications

  • 5+ years of database administration in production environments.
  • Experience with AI-driven monitoring and automation.
  • Strong understanding of security, compliance, and DR concepts.

Responsibilities

  • Support AI-powered monitoring and alerting systems that autonomously detect anomalies and suggest remediation.
  • Develop and maintain LLM-based database agents and knowledge bases.
  • Document incidents, resolutions, and runbooks for AI-driven reference in the RAG workflow.
  • Support root cause analysis workflows using real-time metrics and historical patterns.
  • Maintain and enhance automated playbook engines for self-healing remediation.
  • Refine prompt engineering for database AI agents to improve diagnostics.
  • Feed telemetry into AI/ML pipelines for trend detection and capacity forecasting.
  • Enable natural language query interfaces for database health metrics.

Skills

High availability
On-call support
Root cause analysis

Tools

SQL Server
PowerShell
Python
LLM-based tooling

Job description

Namely is seeking an expert-level Database Operations Engineer to administer and support Cornerstone’s database infrastructure using AI and intelligent automation. You will ensure high availability, security, and performance across production and non-production environments supporting critical apps and automation platforms.

You will drive AI-powered monitoring, automated remediation, and autonomous diagnostics while collaborating on LLM-based database agents, knowledge bases, and incident

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