AI Operations Specialist
AI Operations Specialist
Table of Contents
Study Facts
- Prog. CodeAOS
- NQF Level5.2
- Credits120
- Valid fromV27
- Versionv1.0 (2026-11)
- Study ModeFull-time, Part-time
- Program Manager
AI Operations Specialist is a two year higher vocational education programme focused on the infrastructure, integration and operational practices required to deploy, manage and maintain AI-enabled solutions. The programme prepares students for professional roles involving cloud infrastructure, enterprise integration, AI platforms and governance, with an emphasis on security, reliability, performance and operational costs.
The first year covers the same foundation as the one year Software Development with AI programme: programming, frontend and back-end development, data, product thinking and the integration of AI services into applications. AI is introduced in stages, so students learn to understand and explain code before they rely on tools to produce it. In the second year, students specialise in cloud infrastructure, infrastructure as code, CI/CD, containers, networking, access management and operational reliability. They learn to integrate AI-enabled solutions with business systems through APIs, messaging and automated workflows, including controlled agentic processes and the use of organisational data. Students also configure and manage AI platform services, including model access, data connections, permissions and cost controls.
The programme develops students’ competencies in monitoring, troubleshooting and maintaining AI-enabled solutions. Students evaluate application and service performance, reliability, AI output quality and operational costs, and apply relevant practices for incident management and recovery. They also develop knowledge of AI governance, regulatory requirements, accountability, data protection and information security, and learn to evaluate the business value and feasibility of AI solutions. The programme combines theoretical knowledge with practical activities and projects. It concludes with a project in which students design, deploy and operate an AI-enabled solution within a defined organisational scenario, documenting its architecture, integrations and operational procedures. Upon completion, graduates can contribute to planning, deploying, integrating and maintaining AI-enabled solutions, communicate technical and operational decisions to relevant stakeholders, and support organisations in adopting and operating AI responsibly and reliably.
Students at Noroff access their learning resources through Moodle, our learning management system (LMS). Each course follows a structured progression that guides students through the learning content, activities and assessments, supporting them in gradually developing the knowledge, skills and general competence described in the learning outcomes. Learning resources combine written content, video and practical activities, and learning activities, assignments, assessment criteria and deadlines are all available in the LMS. Throughout the programme, students work both individually and collaboratively.
Microsoft Teams is used for communication and interaction between students and teachers. Students are expected to contribute to an inclusive and supportive learning environment through active participation and collaboration.
Students take an active role in their own learning and are responsible for keeping up with the planned progression. This includes setting aside enough time for their studies, completing learning activities and assignments, and seeking support from teachers and fellow students when needed.
The programme is designed for asynchronous learning, giving students flexibility in how they organise their studies. Students can access learning resources and plan their own study time within the set progression and deadlines. Synchronous activities, such as live sessions, may be provided as additional resources.
English is the primary language for learning resources, communication, delivery and assessment.
Vocational education at Noroff can expand career opportunities and lay lifelong learning foundations. Throughout the programme, students will familiarise themselves with key competencies relevant to industry employment.
Career opportunities
After graduation, the candidate may qualify for work within these areas:
- AI Operations Specialist
- DevOps Engineer
- Platform Engineer
- Cloud Operations Engineer
- Integration Developer
- AI Platform Administrator
- Technical Consultant for AI Solutions
The Norwegian Qualifications Framework for lifelong learning (NQF) defines the levels of qualifications in the Norwegian educational system. These levels describe the knowledge, skills, and competence a learner is expected to achieve as a result of a learning process. Categories in NQF are defined as:
Knowledge: Understanding theories, facts, principles, procedures in the discipline, subject area and/or occupation.
Skills: Ability to utilise knowledge to solve problems or tasks (cognitive, practical, creative and communication skills).
General Competence: Ability to independently utilise knowledge and skills in different situations.
After graduation from this programme, students have acquired the following learning outcomes:
The candidate...
- has knowledge of concepts, theories, models, processes and tools used to build, integrate, deploy and operate software and AI solutions, from programming fundamentals, data storage and APIs to cloud infrastructure, enterprise integration and AI platforms
- can assess their own work in relation to applicable norms and requirements, such as data protection, universal design, information security, regulation of AI and the operational requirements of a service
- has knowledge of the IT industry and is familiar with the work of developers and operations staff, from team practices such as agile development and version control to incident handling and working with AI agents
- can update their knowledge of cloud services, AI models and platforms, operational tools and practices as technology changes
- is familiar with the history, traditions, distinctive nature and place in society of software development and IT operations, including how AI is changing the way systems are built and run
- has insight into their own opportunities for development as a developer or operations specialist, including which skills to strengthen as AI takes over routine tasks
The candidate...
- can apply relevant tools and techniques to configure cloud infrastructure, automate deployment and integrate AI-enabled solutions with existing services and systems
- can explain their choices, from how their own code works to the infrastructure, integrations and AI services in a solution, to both technical and non-technical audiences
- can reflect over their own practice, including how they use AI tools and agents, and adjust it under guidance
- can find and refer to technical documentation, standards and other information, including explanations from AI, and assess its relevance and reliability for a development or operations problem
- can assess operational issues in deployed AI-enabled solutions, identify issues in infrastructure, integrations, security, cost and the quality of AI output, and determine what measures need to be implemented
The candidate...
- can plan and carry out projects alone and as part of a group, involving the deployment, integration and operation of AI-enabled solutions, in accordance with ethical requirements and principles
- can carry out development and operations work based on the needs of selected users and stakeholders, and judge when AI adds value to a solution and when it does not
- can build relations with peers, also across discipline boundaries, and with users and other stakeholders
- can exchange points of view with others in the field and participate in discussions about the development of good practice in building and operating AI solutions
- can contribute to organisational development by identifying where AI can create value for an organisation, and helping to put AI solutions into use and keep them running reliably
|
Course name |
Semester |
Weeks |
Hours |
Credits |
|---|---|---|---|---|
|
Programming and Frontend Development |
1 |
13 |
546 |
20 |
|
Product Engineering & Professional Practice |
1 |
6 |
252 |
10 |
|
Total semester 1 |
|
19 |
798 |
30 |
|
Back-end, APIs & Data |
2 |
7 |
294 |
10 |
|
Applied AI Integration |
2 |
6 |
252 |
10 |
|
Full Stack Application Project |
2 |
6 |
252 |
10 |
|
Total semester 2 |
|
19 |
798 |
30 |
|
Cloud Infrastructure & Platform Engineering |
3 |
11 |
462 |
17.5 |
|
Enterprise AI Platforms & Integration |
3 |
8 |
336 |
12.5 |
|
Total semester 3 |
|
19 |
798 |
30 |
|
AI Operations, Reliability & Observability |
4 |
6 |
252 |
10 |
|
AI Operations Capstone Project |
4 |
13 |
546 |
20 |
|
Total semester 4 |
|
19 |
798 |
30 |
|
|
|
76 |
3,192 |
120 |
Learning activities at Noroff are based on Noroff's Pedagogical framework.pdf and four core principles: industry relevance, practical work, collaboration and guidance. Together, these principles support a student-active learning experience that prepares students for professional working life.
Learning activities are aligned with the learning outcomes and provide students with opportunities to understand, explore, practise and apply what they are learning. Activities may include practical tasks, reflection, problem-solving, discussions, collaborative work, cases and projects. The specific activities vary according to the learning outcomes.
Industry relevance connects learning to professional practice. Students work with methods, tools, cases and problems that reflect current working life.
Practical work gives students opportunities to apply knowledge and develop skills through active engagement.
Collaboration is an important part of the learning process. Collaborative work may be included as part of coursework requirements or assessment, as specified in the course description. Students work with others to exchange ideas, explore different perspectives, solve problems and develop their ability to contribute in a team. Explaining ideas to others, giving and receiving feedback, and taking different roles in collaborative work can support reflection and deeper understanding.
Guidance supports students' learning and progression. Students receive guidance and feedback from teachers and fellow students. Students are expected to actively use feedback to reflect on their work, make improvements and further develop their knowledge and skills.
More detailed information about learning activities is provided in the course descriptions.
Assessment consists of coursework requirements and exams, designed to support student learning and to evaluate whether students have achieved the learning outcomes.
Coursework requirements may be used to assess specific learning outcomes or to ensure participation in essential learning activities. Exams assess whether students have achieved the learning outcomes of the course. Throughout each course, students receive feedback and guidance to support their learning and development.
The assessment methods and coursework requirements for each course are described in the course descriptions.
Information about equipment requirements is available on our website under Programme information.
[The link will be updated when the programme starts.]
There are three ways to meet the admission requirements and be enrolled as a student:
1. Upper secondary education (videregående skole)
- Higher education entrance qualification from Norway or abroad
2. Norwegian vocational upper secondary education
- Documented craft or journeyman’s certificate (fag- og svennebrev) or vocational qualification (yrkeskompetanse) in a relevant programme area (utdanningsprogram), for example:
- Electrical Engineering and Computer Technology (Elektro og datateknologi)
- Technology and Industrial Production (Teknologi- og industrifag)
- Information Technology and Media Production (Informasjonsteknologi og medieproduksjon)
3. Prior learning and work experience (realkompetanse)
- More information about admission requirements is available on our webpage under Admission Requirements.