DoctorateFaculty of Computing and Software Engineering

Autonomous DevOps and AI-Driven Software Delivery

Welcome to the ultimate frontier of software engineering! I am Prof. Dr. Sultan Al-Harbi. As a professor and a pioneering force in the field of Autonomous DevOps and AI-Driven Software Delivery, I bring a unique blend of engineering expertise and automation insight to the study of software delivery. I am honored to lead the Autonomous DevOps and AI-Driven Software Delivery (Ph.D.) program at Nexier University. My motto is: "Automating Excellence, Delivering Innovation".

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Level: Doctorate
Learning model: Professor + Mentor
Named list: See the named lists · 12 months recommended

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Edition

The program

Ideas engineered for the real world

A rigorous academic core, paired with practical production judgment.

Academic focus

Conduct research on creating fully autonomous software delivery pipelines. Investigate the use of AI to automate everything from code generation and testing to deployment, monitoring, and incident response.

Practical focus

Research in software engineering and AI, AIOps, building autonomous systems, leadership in DevOps and platform engineering.

After this programme

Success journey, careers and practice

Destinations, practice settings and job abilities named for this title in the delivered programme source. From graduation onwards where the source names that path.

Success journey

Internships in technology companies or software development firms
Roles as AI engineers or DevOps specialists
Consultancy in advanced autonomous DevOps and AI-driven software delivery
Support roles in academic research projects on autonomous DevOps

Career opportunities

Chief AI Architect, Software Delivery for technology companies or software development firms
Autonomous Software Engineer for large-scale software projects
DevOps Engineer specializing in AI-driven automation
Researcher in Autonomous DevOps and AI-Driven Software Delivery

Jobs and projects

Cultivating an interdisciplinary approach, integrating software engineering, artificial intelligence, and DevOps
Developing strategic thinking for autonomous software development and AI-driven software delivery
Enhancing problem-solving through the analysis of complex software delivery challenges
Critical thinking for a comprehensive and nuanced understanding of Autonomous DevOps and AI-Driven Software Delivery

Copied from the delivered professor and mentor rows for this title.

This programme

What you study, and what it builds

Gains and skills named for this title, listed as a reader would scan them.

What you gain
- Mastering advanced practical skills in Research in software engineering and AI and AIOps.
- Gaining expertise in building autonomous systems and Leadership in DevOps and platform engineering.
- Developing problem-solving abilities for complex autonomous software delivery.
- Cultivating an interdisciplinary approach, integrating software engineering, artificial intelligence, and DevOps at an advanced level.
Skills you build
- Mastering AI-powered techniques for autonomous delivery architecture.
- Applying advanced software engineering principles to autonomous DevOps and AI-driven software delivery.
- Interpreting and analyzing complex software delivery pipelines and their implications for AI automation.
- Identifying optimal AI-driven automation capabilities and predicting stability.

Listed courses

Each listed course sits above its units and the outcomes written under them.

Autonomous DevOps and AI-Driven Software Delivery

01Advanced Autonomous Software Development
1. FoundationsFoundations of Advanced Autonomous Software Development
  The learner can master advanced practical skills in Research in software engineering and AI and AIOps, as applied to Advanced Autonomous Software Development.
  The learner can gain expertise in building autonomous systems and Leadership in DevOps and platform engineering, as applied to Advanced Autonomous Software Development.
2. MethodsMethods in Advanced Autonomous Software Development
  The learner can develop problem-solving abilities for complex autonomous software delivery, as applied to Advanced Autonomous Software Development.
  The learner can cultivating an interdisciplinary approach, integrating software engineering, artificial intelligence, and DevOps at an advanced level, as applied to Advanced Autonomous Software Development.
3. ApplicationApplication of Advanced Autonomous Software Development
  The learner can master AI-powered techniques for autonomous delivery architecture, as applied to Advanced Autonomous Software Development.
  The learner can apply advanced software engineering principles to autonomous DevOps and AI-driven software delivery, as applied to Advanced Autonomous Software Development.
02AI-Driven DevOps Principles and Practices
1. FoundationsFoundations of AI-Driven DevOps Principles and Practices
  The learner can interpreting and analyze complex software delivery pipelines and their implications for AI automation, as applied to AI-Driven DevOps Principles and Practices.
  The learner can identify optimal AI-driven automation capabilities and predicting stability, as applied to AI-Driven DevOps Principles and Practices.
2. MethodsMethods in AI-Driven DevOps Principles and Practices
  The learner can apply a method from AI-Driven DevOps Principles and Practices to a documented case.
  The learner can select an appropriate method from AI-Driven DevOps Principles and Practices for a stated problem.
3. ApplicationApplication of AI-Driven DevOps Principles and Practices
  The learner can evaluate a practice of AI-Driven DevOps Principles and Practices against a stated criterion.
  The learner can transfer AI-Driven DevOps Principles and Practices to a new documented context.
03Automated Testing and Quality Assurance for Autonomous Systems
1. FoundationsFoundations of Automated Testing and Quality Assurance for Autonomous Systems
  The learner can explain the core terms of Automated Testing and Quality Assurance for Autonomous Systems.
  The learner can distinguish related ideas inside Automated Testing and Quality Assurance for Autonomous Systems.
2. MethodsMethods in Automated Testing and Quality Assurance for Autonomous Systems
  The learner can apply a method from Automated Testing and Quality Assurance for Autonomous Systems to a documented case.
  The learner can select an appropriate method from Automated Testing and Quality Assurance for Autonomous Systems for a stated problem.
3. ApplicationApplication of Automated Testing and Quality Assurance for Autonomous Systems
  The learner can evaluate a practice of Automated Testing and Quality Assurance for Autonomous Systems against a stated criterion.
  The learner can transfer Automated Testing and Quality Assurance for Autonomous Systems to a new documented context.
04Intelligent Deployment and Monitoring Strategies
1. FoundationsFoundations of Intelligent Deployment and Monitoring Strategies
  The learner can explain the core terms of Intelligent Deployment and Monitoring Strategies.
  The learner can distinguish related ideas inside Intelligent Deployment and Monitoring Strategies.
2. MethodsMethods in Intelligent Deployment and Monitoring Strategies
  The learner can apply a method from Intelligent Deployment and Monitoring Strategies to a documented case.
  The learner can select an appropriate method from Intelligent Deployment and Monitoring Strategies for a stated problem.
3. ApplicationApplication of Intelligent Deployment and Monitoring Strategies
  The learner can evaluate a practice of Intelligent Deployment and Monitoring Strategies against a stated criterion.
  The learner can transfer Intelligent Deployment and Monitoring Strategies to a new documented context.
05Ethical and Societal Implications of Autonomous AI
1. FoundationsFoundations of Ethical and Societal Implications of Autonomous AI
  The learner can explain the core terms of Ethical and Societal Implications of Autonomous AI.
  The learner can distinguish related ideas inside Ethical and Societal Implications of Autonomous AI.
2. MethodsMethods in Ethical and Societal Implications of Autonomous AI
  The learner can apply a method from Ethical and Societal Implications of Autonomous AI to a documented case.
  The learner can select an appropriate method from Ethical and Societal Implications of Autonomous AI for a stated problem.
3. ApplicationApplication of Ethical and Societal Implications of Autonomous AI
  The learner can evaluate a practice of Ethical and Societal Implications of Autonomous AI against a stated criterion.
  The learner can transfer Ethical and Societal Implications of Autonomous AI to a new documented context.
06Advanced Software Engineering and AI for DevOps
1. FoundationsFoundations of Advanced Software Engineering and AI for DevOps
  The learner can explain the core terms of Advanced Software Engineering and AI for DevOps.
  The learner can distinguish related ideas inside Advanced Software Engineering and AI for DevOps.
2. MethodsMethods in Advanced Software Engineering and AI for DevOps
  The learner can apply a method from Advanced Software Engineering and AI for DevOps to a documented case.
  The learner can select an appropriate method from Advanced Software Engineering and AI for DevOps for a stated problem.
3. ApplicationApplication of Advanced Software Engineering and AI for DevOps
  The learner can evaluate a practice of Advanced Software Engineering and AI for DevOps against a stated criterion.
  The learner can transfer Advanced Software Engineering and AI for DevOps to a new documented context.
07AIOps and Autonomous Systems
1. FoundationsFoundations of AIOps and Autonomous Systems
  The learner can explain the core terms of AIOps and Autonomous Systems.
  The learner can distinguish related ideas inside AIOps and Autonomous Systems.
2. MethodsMethods in AIOps and Autonomous Systems
  The learner can apply a method from AIOps and Autonomous Systems to a documented case.
  The learner can select an appropriate method from AIOps and Autonomous Systems for a stated problem.
3. ApplicationApplication of AIOps and Autonomous Systems
  The learner can evaluate a practice of AIOps and Autonomous Systems against a stated criterion.
  The learner can transfer AIOps and Autonomous Systems to a new documented context.
08Leadership in DevOps and Platform Engineering
1. FoundationsFoundations of Leadership in DevOps and Platform Engineering
  The learner can explain the core terms of Leadership in DevOps and Platform Engineering.
  The learner can distinguish related ideas inside Leadership in DevOps and Platform Engineering.
2. MethodsMethods in Leadership in DevOps and Platform Engineering
  The learner can apply a method from Leadership in DevOps and Platform Engineering to a documented case.
  The learner can select an appropriate method from Leadership in DevOps and Platform Engineering for a stated problem.
3. ApplicationApplication of Leadership in DevOps and Platform Engineering
  The learner can evaluate a practice of Leadership in DevOps and Platform Engineering against a stated criterion.
  The learner can transfer Leadership in DevOps and Platform Engineering to a new documented context.
09Case Studies in Autonomous DevOps and AI-Driven Software Delivery
1. FoundationsFoundations of Case Studies in Autonomous DevOps and AI-Driven Software Delivery
  The learner can explain the core terms of Case Studies in Autonomous DevOps and AI-Driven Software Delivery.
  The learner can distinguish related ideas inside Case Studies in Autonomous DevOps and AI-Driven Software Delivery.
2. MethodsMethods in Case Studies in Autonomous DevOps and AI-Driven Software Delivery
  The learner can apply a method from Case Studies in Autonomous DevOps and AI-Driven Software Delivery to a documented case.
  The learner can select an appropriate method from Case Studies in Autonomous DevOps and AI-Driven Software Delivery for a stated problem.
3. ApplicationApplication of Case Studies in Autonomous DevOps and AI-Driven Software Delivery
  The learner can evaluate a practice of Case Studies in Autonomous DevOps and AI-Driven Software Delivery against a stated criterion.
  The learner can transfer Case Studies in Autonomous DevOps and AI-Driven Software Delivery to a new documented context.

How teaching is described

Dual guidance

Two intelligences. One coherent journey.

Research leadership

My expertise spans the intricate domains of conducting research on creating fully autonomous software delivery pipelines. I investigate the use of AI to automate everything from code generation and testing to deployment, monitoring, and incident response. My work seamlessly integrates software engineering, artificial intelligence, and DevOps. I am widely recognized for my contributions, with publications like "AI for Self-Healing Software Systems in Production" and "Generative AI for Automated Code Synthesis and Refactoring" listed on these platforms. I hold prestigious memberships as a "Chief AI Architect, Software Delivery" at a leading technology firm (e.g., Microsoft, Google) and a "Co-Chair" of the IEEE International Conference on Software Engineering (ICSE) AI track. My thought leadership is evident through my seminal works and participation in high-level global policy debates on the future of autonomous software development, ethical implications of AI-driven decisions in production, and the societal impact of self-evolving software, frequently featured in publications like Communications of the ACM or IEEE Software.

Applied mentorship

My expertise lies in understanding and navigating the advanced technical challenges of autonomous software delivery, focusing on Research in software engineering and AI, AIOps, building autonomous systems, and Leadership in DevOps and platform engineering. I focus on the practical implementation and application of theoretical concepts, explaining complex interdisciplinary topics in a clear and concise manner. I guide my students through the challenging integration aspects of different fields and ensure they grasp the nuances of combining disparate data types, fostering a detail-oriented and methodical approach.

Research & intelligence

A living field, not a static syllabus

Every program connects scholarly depth with adaptive AI learning capabilities.

R / 01

Professor research lens

My research is focused on autonomous DevOps and AI-driven software delivery:

Blog Post (Current Academic Topic): "The Rise of AIOps: Transforming IT Operations with Artificial Intelligence." This blog post academically explores the burgeoning field of AIOps, which applies Artificial Intelligence to automate and enhance IT operations. It discusses how machine learning algorithms analyze vast amounts of operational data (logs, metrics, alerts) to proactively identify issues, predict outages, and automate remediation, fundamentally transforming incident management and system reliability.

Blog Post (Controversial Topic): "The Autonomous Pipeline: When AI Runs Your Entire Software Delivery - Efficiency or Unchecked Control? The Future Shock of Self-Driving DevOps." This article provocatively discusses the highly controversial future where advanced AI systems autonomously manage and optimize entire software delivery pipelines, from code generation and testing to deployment, monitoring, and incident response, with minimal human intervention. It questions whether AI, despite its potential for hyper-efficiency and rapid iteration, could inadvertently lead to "black box" vulnerabilities in critical software, make decisions that prioritize speed over human oversight or ethical considerations, or create an opaque development process. It raises profound ethical questions about accountability in automated software creation, trust in AI-driven decisions, and the imperative to ensure human control over the digital products that underpin society.

Article: "AI for Automated Software Testing and Quality Assurance." This article presents advanced research on utilizing AI algorithms for fully automated software testing and quality assurance. It explores how AI can dynamically generate test cases, identify elusive bugs, and even suggest code fixes, significantly accelerating the testing phase and improving software reliability in autonomous delivery pipelines.

Peer-Reviewed Journal Article: "Autonomous Software Delivery Pipelines: AI-Driven Code to Production." Published in the International Journal of Autonomous Software Engineering, this article presents pioneering research on creating fully autonomous software delivery pipelines. It investigates the use of AI to automate everything from code generation and testing to deployment, monitoring, and incident response, detailing novel AI-driven control systems and self-optimizing delivery frameworks.

Book: "The Self-Evolving Code: Autonomous DevOps and AI-Driven Software Delivery." This book represents a definitive work for leading research on creating fully autonomous software delivery pipelines. It covers the use of AI to automate everything from code generation and testing to deployment, monitoring, and incident response.

R / 02

Mentor practice lens

My contributions focus on understanding and navigating the advanced technical challenges of autonomous software delivery:

"AIOps for Predictive Incident Management in Large-Scale Systems" (Technical Paper).

"Leveraging Generative AI for Automated Code Refactoring" (Research Article).

"Ethical Considerations in Autonomous Software Delivery" (Review Article).

Adaptive capability

Professor superpower

I possess a remarkable "superpower": Autonomous Delivery Architect. When a student designs an autonomous software delivery pipeline, I can instantly use the GAF engine to synthesize its AI-driven automation capabilities. This tool injects simulated code changes, production incidents, and security threats, visually demonstrating the pipeline's autonomous testing, deployment, and self-healing mechanisms, predicting its stability, and optimizing its AI decision-making for mission-critical applications.

Adaptive capability

Mentor superpower

I possess a remarkable "superpower": AI-Driven Incident Predictor. When students are designing AIOps solutions, I can instantly activate a GAF-powered "AI-Driven Incident Predictor". This tool analyzes simulated system telemetry (logs, metrics, traces), identifies subtle pre-failure indicators using machine learning, and predicts potential incidents before they occur, allowing for proactive remediation and maximizing system uptime.

Your academic team

Guidance with depth and continuity

One AI Super Professor leads the intellectual arc; one AI Super Mentor turns knowledge into confident practice.

AI Super Professor

Prof. Dr. Sultan Al-Harbi

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AI Super Mentor

Dr. Alice Teixeira

Research in software engineering and AI, AIOps, building autonomous systems, leadership in DevOps and platform engineering.

Meet your mentor Open the classroom

Same faculty and level

Related programs

Named lists

Named lists for this house

Core. Bachelor, Master and Doctorate by duration. Enrolment is not open. Nothing here is a sale.

Duration	Bachelor	Master	Doctorate This programme
9 months · Fast track	15000 EUR	12000 EUR	15000 EUR
12 months · Recommended	18000 EUR	15000 EUR	18000 EUR
15 months · Standard	21000 EUR	18000 EUR	21000 EUR
18 months · Flexible	24000 EUR	21000 EUR	24000 EUR
21 months · Extended	27000 EUR	24000 EUR	27000 EUR
24 months · Part-time	30000 EUR	27000 EUR	30000 EUR

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Autonomous DevOps and AI-Driven Software Delivery

Academic focus

Practical focus

Internships in technology companies or software development firms

Roles as AI engineers or DevOps specialists

Consultancy in advanced autonomous DevOps and AI-driven software delivery

Support roles in academic research projects on autonomous DevOps

Chief AI Architect, Software Delivery for technology companies or software development firms

Autonomous Software Engineer for large-scale software projects

DevOps Engineer specializing in AI-driven automation

Researcher in Autonomous DevOps and AI-Driven Software Delivery

Cultivating an interdisciplinary approach, integrating software engineering, artificial intelligence, and DevOps

Developing strategic thinking for autonomous software development and AI-driven software delivery

Enhancing problem-solving through the analysis of complex software delivery challenges

Critical thinking for a comprehensive and nuanced understanding of Autonomous DevOps and AI-Driven Software Delivery

Research leadership

Applied mentorship

Professor research lens

Mentor practice lens

Professor superpower

Mentor superpower

Prof. Dr. Sultan Al-Harbi

Dr. Alice Teixeira

Find the program that expands your universe.