How to Be Artificial Intelligence DevOps Engineer - Job Description, Skills, and Interview Questions

Jan 29, 2020 / 6 Minutes Read / By Albert

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Artificial Intelligence (AI) DevOps Engineers are responsible for developing and deploying applications and systems that use AI technologies. This role is essential for organizations that want to leverage the power of AI to create competitive advantages. As AI DevOps Engineers work with AI technologies, their work has a direct impact on the effectiveness of the applications and systems they create.

By optimizing their development and deployment process, they ensure that their applications and systems perform at their peak efficiency, leading to improved customer experience, better business outcomes, and cost savings for the organization. Furthermore, AI DevOps Engineers are required to keep up with the constantly changing landscape of AI technologies, making sure their applications and systems remain up-to-date with the latest advancements. This requires an in-depth understanding of AI concepts, as well as a good working knowledge of DevOps tools and techniques.

Steps How to Become

Earn a Bachelors Degree. The first step to becoming an Artificial Intelligence (AI) DevOps Engineer is to earn a bachelors degree in computer science, engineering, or a related field. This will provide you with the foundational knowledge and skills necessary to pursue a career in AI.
Complete Relevant Coursework. It is important to complete coursework that is relevant to your future career. This includes courses in AI, robotics, machine learning, natural language processing, and other related topics.
Obtain Work Experience. While most employers prefer candidates with experience, some will consider those who are just starting out. Consider pursuing internships or volunteer opportunities that provide hands-on experience working with AI tools and systems.
Advance Your Knowledge. AI is constantly evolving, so it is important to stay up-to-date on the latest advancements in the field. Consider attending seminars or workshops to learn more about AI technologies, as well as reading industry publications and blogs.
Join Professional Organizations. Joining professional organizations such as the Association for Computing Machinery (ACM) can help you stay connected with industry professionals and build your network.
Pursue Certifications. Earning certifications in AI can help demonstrate your knowledge and skills to prospective employers. Some organizations offer certifications, such as the International Association for Artificial Intelligence Certification (IAAIC) or the Association for the Advancement of Artificial Intelligence (AAAI).
Seek Out Job Opportunities. Once you have the necessary education, experience, and certifications, you can begin applying for jobs as an AI DevOps Engineer. Consider searching job boards, networking with industry professionals, or attending job fairs to find opportunities.

The rise of Artificial Intelligence (AI) has revolutionized the DevOps industry, allowing engineers to automate more processes and become more efficient. By utilizing AI tools, DevOps engineers can implement more precise automation techniques that save time and resources, while also reducing the risk of errors and downtime. AI-driven DevOps also allows for more proactive monitoring and faster detection of any issues that may arise.

This, in turn, leads to quicker resolution and improved customer satisfaction. AI-driven DevOps facilitates the development of better software systems, as engineers are able to analyze data faster and more accurately, leading to more effective and efficient product development. AI-driven DevOps helps engineers become skilled and efficient, allowing them to develop better software systems and deliver better customer experiences.

You may want to check Artificial Intelligence Engineer, Artificial Intelligence Strategist, and Artificial Intelligence Automation Engineer for alternative.

Job Description

Design, develop and maintain AI-based DevOps solutions.
Automate and optimize DevOps processes using AI technologies.
Monitor and analyze DevOps performance using AI-driven analytics.
Develop and maintain AI-based models and algorithms for DevOps automation.
Collaborate with other AI engineers, Data Scientists and DevOps engineers to optimize DevOps processes.
Research and develop new AI technologies and algorithms for DevOps automation.
Stay up-to-date with the latest trends in AI and DevOps automation.
Troubleshoot and debug AI-based DevOps solutions.
Document AI-based DevOps solutions and processes.
Analyze and evaluate existing DevOps processes for potential AI-driven automation.

Skills and Competencies to Have

Expertise in AI and Machine Learning technologies, such as TensorFlow, PyTorch, and Scikit-Learn.
Extensive experience with DevOps automation tools such as Ansible, Chef, Puppet, and Jenkins.
Knowledge of cloud computing platforms such as AWS, Microsoft Azure, and Google Cloud Platform.
Ability to write code in Python, C++, Java, or other programming languages.
Knowledge of artificial neural networks and deep learning algorithms.
Understanding of software engineering best practices, including test-driven development and Agile methodology.
Experience with container technologies such as Docker and Kubernetes.
Proficiency in data engineering and analysis, including the use of SQL and NoSQL databases.
Ability to troubleshoot complex issues related to AI applications.
Strong communication and problem-solving skills.

Artificial Intelligence DevOps Engineer is a highly sought-after and specialized role that requires a unique blend of technical and non-technical skills. Primarily, a successful Artificial Intelligence DevOps Engineer must have a solid understanding of software engineering, computer science, and artificial intelligence (AI) principles. They must also possess strong problem solving and communication skills to be able to effectively collaborate with colleagues and stakeholders on projects.

they must have experience with DevOps practices such as continuous integration and deployment, automated testing, and continuous monitoring. Being able to utilize configuration management tools such as Puppet, Chef, Jenkins, and Ansible are also necessary skills to have. Lastly, familiarity with cloud computing platforms such as AWS, Azure, and Google Cloud Platform (GCP) is essential for a successful Artificial Intelligence DevOps Engineer.

All of these skills are necessary for a successful DevOps career in AI and will allow the engineer to efficiently build, deploy, test, and maintain applications that utilize AI.

Artificial Intelligence UX/UI Designer, Artificial Intelligence Sales Engineer, and Artificial Intelligence Technician are related jobs you may like.

Frequent Interview Questions

What experience do you have in artificial intelligence devops engineering?
How familiar are you with the various AI development tools and frameworks?
What challenges have you faced in designing and implementing AI devops solutions?
How do you manage to stay current on the latest industry trends?
What strategies do you use to ensure successful deployment of AI devops projects?
What techniques do you employ to troubleshoot issues during AI devops implementation?
Tell us about a time when you had to work with stakeholders to ensure a successful AI devops project roll-out.
How do you prioritize AI devops tasks across multiple projects?
Can you provide an example of a project where you had to work collaboratively to achieve a successful outcome?
What strategies do you use to ensure security and scalability of AI devops solutions?

Common Tools in Industry

Ansible. An open source automation platform that enables DevOps teams to automate infrastructure, applications, and processes. (eg: Automating the deployment of a web application to multiple servers)
Puppet. An automated configuration management tool used to deploy, manage, and maintain servers. (eg: Enabling automatic updates for servers)
Chef. A configuration management tool that helps provision, configure, and manage servers. (eg: Automating the deployment of software packages to a server)
Kubernetes. A container orchestration tool that automates deployment, scaling, and management of containerized applications. (eg: Automatically scaling an application based on demand)
Jenkins. An open source CI/CD platform that automates the building, testing, and deployment of applications and services. (eg: Automatically deploying a new version of an application to production)

Professional Organizations to Know

Association for the Advancement of Artificial Intelligence (AAAI)
Institute of Electrical and Electronics Engineers (IEEE)
International Joint Conference on Artificial Intelligence (IJCAI)
International Conference on Machine Learning (ICML)
American Association for Artificial Intelligence (AAAI)
Association for Computing Machinery (ACM)
International Society for Artificial Life (ISAL)
Association for Unmanned Vehicle Systems International (AUVSI)
DevOps Institute (DOI)
Continuous Delivery Foundation (CDF)

We also have Artificial Intelligence Architect, Artificial Intelligence Business Development Manager, and Artificial Learning System Administrator jobs reports.

Common Important Terms

Machine Learning. A form of artificial intelligence that uses data to identify patterns and build models to make predictions or decisions.
Deep Learning. A subset of machine learning that uses artificial neural networks to learn from data.
Natural Language Processing (NLP). A field of computer science that uses algorithms to process and understand natural language.
Automation. The process of automating tasks or processes through computer programs.
DevOps. A set of practices and tools for development, operations and testing to create a continuous delivery pipeline for software products.
Cloud Computing. The delivery of computing services such as storage, databases, networking, analytics, and more over the internet.
Containerization. The process of packaging an application so it can be deployed in different environments without changes.
Data Science. A field of science that uses data to gain insights and knowledge.

Frequently Asked Questions

Q1: What is a Artificial Intelligence DevOps Engineer? A1: An Artificial Intelligence DevOps Engineer is a professional who specializes in the development and maintenance of automation systems, software, and infrastructure that support artificial intelligence (AI) applications. Q2: What skills are needed to become an Artificial Intelligence DevOps Engineer? A2: To become an Artificial Intelligence DevOps Engineer, one needs to have a strong understanding of computer science and software engineering principles as well as experience with DevOps tools and processes, such as automation, continuous integration and delivery, and containerization. Additionally, having knowledge of cloud computing and AI technologies is beneficial. Q3: What is the average salary for an Artificial Intelligence DevOps Engineer? A3: The average salary for an Artificial Intelligence DevOps Engineer can vary depending on the location, experience level, and other factors. However, according to Indeed.com, the average salary for an Artificial Intelligence DevOps Engineer in the United States is around $106,227 per year. Q4: What is the job outlook for an Artificial Intelligence DevOps Engineer? A4: The demand for Artificial Intelligence DevOps Engineers is expected to grow significantly in the coming years, due to the increasing need for automation and AI-based solutions. According to the U.S. Bureau of Labor Statistics, jobs in this field are projected to increase by 13 percent through 2026. Q5: What types of organizations hire Artificial Intelligence DevOps Engineers? A5: Organizations that utilize artificial intelligence and automation technologies often hire Artificial Intelligence DevOps Engineers. This includes tech companies, businesses with large IT departments, research institutions, and other organizations that need to build, maintain, and optimize AI-based systems.

Web Resources

Artificial Intelligence Engineering | Software Engineering Institute www.sei.cmu.edu
How to Become an Artificial Intelligence Engineer [Career Guide] onlinedegrees.sandiego.edu
How to Become an Artificial Intelligence Developer - University of San onlinedegrees.sandiego.edu