Artificial Intelligence Jobs in Saint Paul, MN

Leads product vision, roadmap, backlog management, prioritization, and Agile delivery for AI-enabled workflow transformation in Global Corporate Trust. Identifies process improvement opportunities, translates stakeholder needs into scalable solutions, coordinates testing and delivery, evaluates emerging AI capabilities, and drives change management, readiness, and adoption. Partners with business and technology teams to deliver governed AI solutions that improve productivity, decision-making, operational efficiency, and stakeholder experiences.

Leads enterprise clinical digital transformation strategy, advancing AI-enabled capabilities, workflow modernization, intelligent automation, and operational improvement across Utilization Management, Care Management, and Whole Health. Partners with digital, AI, technology, product, engineering, and clinical teams to develop and scale solutions. Oversees strategic initiatives, governance, partnerships, consultants, innovation roadmaps, and executive recommendations while driving measurable improvements in quality, cost, productivity, and member and provider experiences.

Sell Infoblox networking and security solutions to enterprise accounts in Nebraska/Minnesota. Hunt new logos, expand existing accounts, manage full sales cycle, collaborate with BDR, marketing, and solutions architects, meet quota, articulate AI/ML solution value, and use AI tools to improve prospecting and forecasting.

Leads global custody operations and the front-to-back servicing of client assets across markets, currencies, and asset classes. Oversees settlements, corporate actions, reconciliations, asset segregation, sub-custodian relationships, regulatory compliance, risk controls, resilience, client escalations, budgets, and performance metrics. Drives transformation from batch processing to real-time exception management through automation, cloud, AI, and ISO 20022. Builds leadership depth, workforce strategy, operational capacity, and scalable service delivery.

Leads Workday HCM engineering and configuration teams, providing technical direction, mentorship, architecture governance, delivery oversight, incident resolution, and engineering standards. Directs Workday implementations, enhancements, integrations, testing, monitoring, and support while partnering with HR, Finance, and IT stakeholders. Drives AI-enabled workforce experiences, intelligent automation, platform improvements, and scalable HR technology solutions. Requires strong software engineering leadership, Workday expertise, Agile delivery experience, stakeholder communication, and strategic problem-solving.

The Senior Data & AI Engineer will design, build, and deploy complex data solutions, focusing on AI and data management. Responsibilities include creating data pipelines, enhancing data quality, and supporting analytics and machine learning initiatives.

Lead design, automation, and modernization of enterprise IBM DB2 and PostgreSQL platforms. Build automation frameworks (Python, Ansible, Terraform, shell, APIs), implement IaC across Azure/AWS, enable self-service provisioning, run upgrades/migrations and HA, and apply AI-driven operational tools for incident triage, performance diagnostics, capacity forecasting, and self-healing automation to improve reliability and reduce operational toil.

Research, design, and implement foundation models for machine-generated and graph data; develop distributed training and inference pipelines; collaborate with engineering and product teams to productionize models; evaluate new AI/ML techniques and drive MLOps, monitoring, and model optimization to improve scalability and reliability.

Lead architecture, design, and delivery of enterprise AI solutions for Legal, Compliance, Procurement, and Regulatory stakeholders. Translate legal challenges into scalable, secure, governed AI architectures integrated with CLM platforms and enterprise systems. Hands-on role: design, develop, validate, and deploy production AI agents, retrieval systems, orchestration services, APIs, integrations, and workflow automation while establishing governance, monitoring, and technical standards.

Design, build, and deliver AI-powered enterprise solutions using LLMs, cloud platforms, and container orchestration. Lead end-to-end delivery, partner with stakeholders, mentor junior engineers, and resolve complex incidents while ensuring production-ready solutions.

Develop, test, debug, and maintain microservice-based applications; implement features and integrations using Java, Spring Boot, TypeScript/React, Node.js and AWS; participate in incident resolution, impact analysis, and team delivery practices; document work, monitor system health, and continuously expand technical skills.

Design, develop, test, operate, and maintain backend AI-powered services. Produce production-ready code, ensure scalability, reliability, security, and compliance. Implement LLM/RAG solutions, integrate AI services with enterprise systems, participate in code reviews, and follow DevOps and SRE best practices while collaborating across product, architecture, and business teams.

Lead design and delivery of production-grade enterprise AI solutions, drive architecture and engineering standards, mentor engineers, integrate LLMs and agentic workflows, ensure scalability, observability, and secure-by-design practices, and partner with Lines of Business to translate technical capabilities into business value.

Design, build, and deliver enterprise AI solutions using LLMs, cloud platforms, and container orchestration. Own end-to-end delivery, partner with stakeholders, coach junior engineers, and produce production-ready integrations, prototypes, and automations.

Design, implement, test, and maintain cloud-native software solutions. Troubleshoot complex defects, build APIs and integrations, enable AI/LLM capabilities, mentor junior engineers, contribute to CI/CD and observability, and collaborate with cross-functional teams to deliver reliable systems.

Lead design and delivery of production-grade generative AI platform capabilities. Drive architecture and system design, implement cloud-native, containerized solutions (Python, Kubernetes, Terraform), ensure CI/CD, observability, and secure-by-design practices, and lead cross-functional stakeholder and engineering efforts to deliver enterprise AI applications.