AI Engineer

Hybrid remote position for candidates residing along the Wasatch Front. Occasional on-campus attendance required for essential meetings and events. May require occasional travel.

About the role

The Utah Data Coordinating Center (DCC) is seeking an experienced AI Engineer to design, build, and operate secure, scalable AI-enabled research platforms. This role sits at the intersection of machine learning, cloud infrastructure, and regulated research environments, supporting national and international research programs. You will work closely with research IT leadership, data engineers, security teams, and external partners to operationalize AI workflows while maintaining strong governance, security, and compliance standards. This is a hands-on engineering role for someone who enjoys building real systems, not prototypes that live on slides. This position will report to the Sr. Supervisor, IT.

Responsibilities

• Design and deliver end-to-end hybrid data and AI solutions.
• Collaborate with data scientists, engineers, and business stakeholders to design, build, test, deploy, and support scalable data pipelines and AI/ML models across hybrid cloud and on-premises environments.
• Deliver reliable, production-ready solutions aligned with organizational strategy and enterprise architecture standards.
• Operationalize machine learning systems using modern MLOps practices.
• Partner with cross-functional teams to deploy, monitor, and manage ML models through CI/CD pipelines, model versioning, experiment tracking, automated testing, and lifecycle management frameworks.
• Support both batch and real-time inference workloads while ensuring reliability, scalability, and maintainability.
• Build and maintain secure, scalable infrastructure across hybrid environments.
• Implement containerized and cloud-native solutions using Docker and orchestration platforms (e.g., Kubernetes) to support data and AI workloads.
• Apply infrastructure-as-code (IaC) and automation practices to enable reproducibility, scalability, and operational efficiency across on-premises and cloud systems.
• Ensure secure, compliant, and governed data and AI systems.
• Collaborate with security and compliance teams to implement role-based access controls, encryption, network security controls, and audit logging across environments.
• Align architectures with regulatory frameworks (e.g., HIPAA, NIST, FISMA) and enterprise governance standards while promoting responsible AI practices.
• Translate business requirements into scalable technical architectures.
• Engage with stakeholders to understand strategic objectives and convert them into robust data architectures, algorithms, and automation workflows.
• Promote shared ownership of solutions, ensuring alignment with long-term sustainability, performance expectations, and enterprise standards.
• Monitor, optimize, and sustain production systems.
• Implement monitoring, logging, and alerting frameworks to track data pipeline health, model performance, data drift, system reliability, and cost efficiency.
• Apply performance tuning, reliability engineering, and continuous improvement practices to maintain operational excellence.
• Communicate technical designs and analytical insights clearly.
• Document system architectures, data flows, AI workflows, and operational procedures.
• Present complex technical concepts and model outcomes to both technical and non-technical stakeholders in a clear and actionable manner.
• Advance engineering excellence and innovation.
• Stay current with emerging technologies in data engineering, cloud computing, and applied AI.
• Evaluate and adopt new tools and methodologies that improve automation, scalability, security, and organizational impact while adhering to best practices and architectural standards.
• Research, design, develop, test, and support artificial intelligence (AI) and machine learning (ML) frameworks and models.
• Leverage AI/ML techniques to answer business questions, support business strategies, and deliver valuable quantitative insights to improve products.
• Develop sophisticated algorithms to automate processes and tasks.
• Collaborate with internal stakeholders to understand business and technical needs.
• Code and develop software that deploys ML models and algorithms into production.
• Communicate and present complex analytics results and concepts to leadership and internal stakeholders.
• Employ AI and/or ML that may include natural language processing (NLP), natural language understanding (NLU), semantic understanding, intent classification, computer vision, deep learning, and automatic speech recognition (ASR).
• Remain up to speed on cutting edge research for AI technology and concepts.

Requirements

• Artificial Intelligence (AI) / Machine Learning (ML) Engineer, III: Requires a bachelor’s degree (or equivalency) + 6 years or a master’s degree (or equivalency) + 4 years of directly related work experience.
• Artificial Intelligence (AI) / Machine Learning (ML) Engineer, IV: Requires a bachelor’s degree (or equivalency) + 8 years or a master’s degree (or equivalency) + 6 years of directly related work experience.
• 1 year of higher education can be substituted for 1 year of directly related work experience.

Preferences

• Experience with MLOps platforms or custom ML deployment pipelines.
• Familiarity with vector databases, embeddings, or RAG-based systems.
• Experience supporting clinical research, biomedical data, or sensitive datasets.
• Familiarity with compliance-driven environments (FISMA Moderate/High, HITRUST, etc.).
• Experience working in academic or research institutions.
• Strong experience with Python and modern ML/AI tooling.
• Hands-on experience with AWS (or comparable cloud platforms).
• Experience building and operating CI/CD pipelines.
• Proficiency with Docker and container-based workflows.
• Experience supporting data-intensive or research computing environments.
• Strong understanding of security best practices in regulated environments.
• Ability to work independently and communicate clearly with both technical and non-technical stakeholders.

Pay

$90,188 to $135,601 per year. The department may hire at either of the following levels with associated pay ranges:

• AI/ML Engineer, III: $90,188 to $123,274
• AI/ML Engineer, IV: $99,587 to $135,601

Schedule

Full-time, 40 hours per week. Monday through Friday from 8:00 am to 5:00 pm.

Benefits

The University of Utah offers a comprehensive benefits package. Learn more about the benefits of working at the University of Utah.