DevOps Engineer, AI Platform
Ultimus Fund Solutions
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- Pay
- $100K to $250K
- a year
- Location
- Columbus, OH
- Work style
- On-site
- Job type
- Other
- Posted
- Nov 7
- 1 day ago
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Career path
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About the company
Ultimus Fund Solutions
Financial services · 501 to 1,000 employees
What you'll do
- Build and maintain infrastructure as code for the AI platform on Azure
- Create CI/CD pipelines for AI applications, agents, models and prompts
- Set up separate development, test and production environments
- Implement monitoring, logging, tracing and alerting for AI applications and model usage
- Manage releases, rollbacks and incident response
- Support the platform's kill-switch and change-control processes
- Automate routine operations and keep the platform patched, scalable and resilient
Key skills
AzureAKSAzure DevOpsinfrastructure as codeCI/CD pipelinesautomated testingmonitoringloggingtracingalertingincident response
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About the team
Ultimus is investing in AI to transform how fund administration work gets done, from generative AI assistants, intelligent document processing and machine learning to a growing set of agentic AI workflows. The AI Infrastructure team builds and runs the secure, shared platform that all of our AI solutions are built on, giving teams across the firm a consistent, well-governed foundation for building, deploying and scaling AI applications, models and agents. You will join early and help shape the platform and practices the rest of the firm builds on.
About the role
As DevOps Engineer you build and run the delivery pipeline and runtime for our AI platform, so the AI Development pods can build, test and release AI applications, models and agents quickly and safely.
What you'll do
Build and maintain infrastructure as code for the AI platform on Azure (AKS, container registry, API Management, networking and supporting services).
Create CI/CD pipelines in Azure DevOps for AI applications, agents, models and prompts, including automated testing and evaluation gates before release.
Set up separate development, test and production environments so teams can build and run controlled pilots.
Implement monitoring, logging, tracing and alerting for AI applications, agents and model usage, including cost and token tracking.
Manage releases, rollbacks and incident response, and support the platform's kill-switch and change-control processes.
Automate routine operations and keep the platform patched, scalable and resilient.