Adjunct Instructor: AI Cloud and DevOps

Carnegie Mellon University; Pittsburgh, Northern (Allegheny County, KY); Hybrid; USD 6,000 - 10,000; Part time

Job summary

The Heinz College of Information Systems and Public Policy at Carnegie Mellon University seeks an adjunct instructor for an AI Cloud and DevOps course in the AIM program. The course focuses on productionization and operation of AI systems in cloud environments, including cloud infrastructure, DevOps, MLOps, and LLMOps, with labs and real-world applications.

You should bring hands-on experience deploying AI systems in cloud platforms; recent teaching experience is preferred.

Qualifications

• The instructor should be a practitioner with direct experience in deploying and operating AI systems within cloud environments.
• Recent teaching experience is preferred.

Responsibilities

• Teach the AI Cloud and DevOps course as an adjunct instructor.
• Develop labs, case studies, readings, and assignments to assess mastery of concepts.
• Evaluate student work and provide feedback within the AIM program.

Job description

Location

Pittsburgh, PA

Open Date

Jan 22, 2026

Description

The Heinz College of Information Systems and Public Policy at Carnegie Mellon University seeks an adjunct instructor for AI Cloud and DevOps for students in the Master of Science in Artificial Intelligence Systems Management (AIM) program.

This course focuses on the productionization and operation of AI systems in cloud environments, examining the cloud infrastructure and DevOps practices required to deploy, scale, monitor, and govern AI applications in real-world organizational settings. Students study how machine learning models, large language models (LLMs), data pipelines, and emerging AI agents move from experimentation to reliable, maintainable production systems on modern cloud platforms.

Adopting a systems and lifecycle perspective, the course integrates concepts from cloud computing, DevOps, MLOps, and LLMOps. Topics include : cloud-native AI architectures; containerization and orchestration; CI/CD for data and model workflows; managed AI services across major cloud providers (e.g., AWS, Azure, and Google Cloud); monitoring and observability; operational risk management; and cost-performance trade-offs. The course also emphasizes how responsible AI principles, such as safety, accountability, security, and compliance, are embedded into operational workflows, particularly for LLM-based systems.

Through applied labs, case studies, and design-oriented assignments, students shouldgain hands-on experience deploying and operating AI systems within real cloud environments, including LLM-powered services and agent-based pipelines.

Recognizing AI Cloud and DevOps is a broad and complex topic, the minimum goals of this course are to prepare students to explain how AI systems are operationalized in modern cloud environments, to design end-to-end architectures for production AI systems, to apply DevOps, MLOps, and LLMOps principles across the AI lifecycle, to deploy and operate AI systems using major cloud platforms, to evaluate operational risks in deployed AI systems, to monitor and manage AI systems post-deployment, to assess security, privacy, and governance considerations in AI operations, to analyze trade-offs among scalability, reliability, performance, and cost, and to communicate AI system design and operational decisions to a multitude of stakeholders..

The course is a core course for students in the AIM program in their third, and final semester, of the program. The instructor should assume that the students have some baseline knowledge of Machine learning, Large Language Models, Data Pipelines, and emerging AI Agents. The instructor should be a practitioner with direct experience in the field of deploying and operating AI systems within cloud environments. Recent experience in teaching is preferred.

The course is a half semester (i.e. 7 weeks) during either the summer semester (June 22 - July 31) . Course times could be afternoons (two 80 minute class sessions per week) or evenings (one 170 minute class from 6:30-9:20 PM, inclusive of a break, per week), as preferred.

The course design should at minimum include relevant readings (textbook, research papers, news articles, etc.), in-class discussions, and appropriate evaluations of mastery of concepts for grading purposes (homework, quizzes/exams, etc.). Given the focus of Heinz College graduate programs, utilization of data, strategic thinking, and application of leadership skills are highly encouraged to be integrated into the course.

About Heinz College

The Heinz College of Information Systems and Public Policy is home to two internationally recognized schools: the School of Information Systems and Management and the School of Public Policy and Management. The unique colocation of these two schools sets Heinz College apart to tackle society’s most complex problems by teaching our students a firm understanding of policy, technology and analytical foundations, and the management skills to deploy solutions for maximum impact - the intersection of people, policy, and technology to approach complex societal problems.For more information, please visit www.heinz.cmu.edu

Qualifications

The instructor should be a practitioner with direct experience in the field of deploying and operating AI systems within cloud environments. Recent experience in teaching is preferred.

Application Process

Carnegie Mellon Universityis an equal opportunity employer. It does not discriminate in admission, employment, or administration of its programs or activities on the basis of race, color, national origin, sex, disability, age, sexual orientation, gender identity, pregnancy or related condition, family status, marital status, parental status, religion, ancestry, veteran status, or genetic information. Furthermore, Carnegie Mellon University does not discriminate and is required not to discriminate in violation of federal, state, or local laws or executive orders.