Senior AI Engineer: Enterprise ML & MLOps Leader

Ultimus Fund Solutions; Minneapolis (MN); On-site; USD 150,000 - 200,000; Full time

Job summary

Ultimus Fund Solutions in Minneapolis seeks a Senior AI Engineer to own end-to-end lifecycle of enterprise AI models, from data pipelines to deployment and monitoring, partnering with AI Data Scientists and AI Developers to operationalize models and govern data.

You will lead scalable ML infrastructure, implement MLOps, mentor engineers, and align technical priorities with business goals, delivering measurable improvements in system latency and reliability in a financial services context.

Qualifications

• 7+ years of software engineering, data engineering, or ML operations experience.
• Ability to architect scalable data pipelines and deliver measurable outcomes.
• Deep expertise in Python, SQL, and enterprise cloud platforms.
• Ability to translate operational needs into technical architecture.
• Experience in financial services or fund administration preferred but not required.

Responsibilities

• Architect and scale machine learning pipelines to support automated workflows across Fund Accounting, Transfer Agency, and Onboarding.
• Oversee the development and deployment of robust AI infrastructure leveraging enterprise data lakes and cloud platforms.
• Lead technical initiatives to integrate AI capabilities into existing legacy systems and financial platforms.
• Establish MLOps best practices and technical KPIs tied to system uptime, model latency, and infrastructure efficiency.
• Mentor junior engineering staff and advise leadership on technical priorities and AI governance.

Skills

Python

SQL

Cloud platforms

Data pipelines

MLOps

Technical leadership

Mentoring

Job description

Ultimus Fund Solutions in Minneapolis seeks a Senior AI Engineer to own end-to-end lifecycle of enterprise AI models, from data pipelines to deployment and monitoring, partnering with AI Data Scientists and AI Developers to operationalize models and govern data.

You will lead scalable ML infrastructure, implement MLOps, mentor engineers, and align technical priorities with business goals, delivering measurable improvements in system latency and reliability in a financial services context.