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In office (Columbus, Cincinnati)

Confirmed on the employer's own hiring board on Nov 10, 2026. First seen by Alion on Nov 6, 2026. Ultimus Fund Solutions scores B on the Alion truth index.

Overview
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Impact
Profile match
Ultimus Fund Solutions is an independent provider of fund administration and investment operations solutions for advisors and asset/fund managers. The firm delivers high-touch, scalable services across public and private markets - including transfer agency, middle-office and investment operations, ETF launch and conversion support, and specialized administration for mutual funds, private equity, private credit, retail alternatives, REITs and other fund wrappers. Ultimus combines these services with integrated, AI-enabled technology platforms (Ultimus Connect, Portfolio Analytics Portal, Workflow Manager, Investor and Advisor Portal, Private Investor Portal and related tools) to provide data, reporting and workflow automation.

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.

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