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AI Project Manager

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AI Project Manager is where the theoretical world of AI meets the practical world of project management. AI Project Managers are responsible for managing projects involving artificial intelligence systems, ensuring they are successfully deployed and used. This career is highly in-demand as businesses look to incorporate more AI into their operations. If you're interested in a career at the forefront of technology, AI Project Manager may be the right fit for you. Here's an overview of the AI Project Manager role, its responsibilities, and how to get started in the field.

Responsibilities

The primary responsibility of an AI Project Manager is to lead and manage AI projects from start to finish. This includes:

  • Planning and budgeting for the project
  • Leading and motivating a team of AI engineers and scientists
  • Managing the project schedule and ensuring milestones are met
  • Monitoring the project budget and ensuring that it is on track
  • Reporting on the project's progress to stakeholders

In addition to these core responsibilities, AI Project Managers may also be responsible for:

  • Developing and implementing AI strategies
  • Working with vendors and partners on AI projects
  • Conducting research on AI technologies

Education and Background

Read more

AI Project Manager is where the theoretical world of AI meets the practical world of project management. AI Project Managers are responsible for managing projects involving artificial intelligence systems, ensuring they are successfully deployed and used. This career is highly in-demand as businesses look to incorporate more AI into their operations. If you're interested in a career at the forefront of technology, AI Project Manager may be the right fit for you. Here's an overview of the AI Project Manager role, its responsibilities, and how to get started in the field.

Responsibilities

The primary responsibility of an AI Project Manager is to lead and manage AI projects from start to finish. This includes:

  • Planning and budgeting for the project
  • Leading and motivating a team of AI engineers and scientists
  • Managing the project schedule and ensuring milestones are met
  • Monitoring the project budget and ensuring that it is on track
  • Reporting on the project's progress to stakeholders

In addition to these core responsibilities, AI Project Managers may also be responsible for:

  • Developing and implementing AI strategies
  • Working with vendors and partners on AI projects
  • Conducting research on AI technologies

Education and Background

AI Project Managers typically have a bachelor's degree in computer science, engineering, or a related field. Additionally, many AI Project Managers have a master's degree in Artificial Intelligence or a related discipline.

As with many roles in the field of AI, certifications are also becoming increasingly common for AI Project Managers. There are a number of different AI certifications available, but some of the most popular include the AI Project Manager certification from the Artificial Intelligence Institute and the Certified AI Project Manager certification from the Project Management Institute.

Skills

AI Project Managers need a combination of technical skills and project management skills. Technical skills include:

  • Strong knowledge of AI technologies and algorithms
  • Experience with machine learning and deep learning
  • Data analysis and data mining skills
  • Cloud computing skills

Project management skills include:

  • Project planning and management
  • Team leadership and management
  • Budgeting and financial management
  • Stakeholder management

Career Path

There are a number of different ways to become an AI Project Manager. Some people start their careers in software engineering or data science and then transition into project management. Others start their careers in project management and then specialize in AI. There is no one-size-fits-all path to becoming an AI Project Manager, but the following steps can help you get started:

  • Earn a bachelor's degree in computer science, engineering, or a related field.
  • Gain experience in software engineering, data science, or project management.
  • Get certified in AI project management.
  • Build a portfolio of AI projects.
  • Network with other AI professionals.

Online Courses

Online courses provide learners with the opportunity to acquire essential skills in AI and project management at their own pace, bolstering the chances of success for entering this career. These courses often incorporate videos, lectures, interactive labs, quizzes, assignments, and discussions to support comprehension.

Particularly when combined with offline, hands-on experience, online courses can provide a solid foundation for learners seeking to transition to this field.

However, it's important to note that while online courses play a valuable role, they may not always be sufficient on their own. Gaining practical experience and networking with other professionals remain crucial for career advancement.

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Salaries for AI Project Manager

City
Median
New York
$200,000
San Francisco
$217,000
Seattle
$229,000
See all salaries
City
Median
New York
$200,000
San Francisco
$217,000
Seattle
$229,000
Austin
$197,000
Toronto
$140,000
London
£109,000
Paris
€86,000
Berlin
€142,000
Tel Aviv
₪61,000
Singapore
S$190,000
Beijing
¥301,000
Shanghai
¥1,118,000
Shenzhen
¥640,000
Bengalaru
₹3,218,000
Delhi
₹1,262,000
Bars indicate relevance. All salaries presented are estimates. Completion of this course does not guarantee or imply job placement or career outcomes.

Path to AI Project Manager

Take the first step.
We've curated nine courses to help you on your path to AI Project Manager. Use these to develop your skills, build background knowledge, and put what you learn to practice.
Sorted from most relevant to least relevant:

Reading list

We haven't picked any books for this reading list yet.
Written by a leading expert in AI, this book offers strategic insights and technical guidance for AI project implementation. Provides a roadmap for navigating the challenges and complexities of real-world AI projects.
A timely and important book that makes the case for using AI for social good. The book includes a series of concrete recommendations for how AI can be used to address the world's most pressing problems.
An introduction to the numerous social challenges that AI can help solve, including climate change, poverty, and healthcare access. The book is written by a professor at the University of Illinois who leading researcher in the field.
A forward-looking exploration of the potential of AI for social good. The book includes essays from leading AI researchers on the most promising areas for future research and development.
A comprehensive overview of the ways that AI is being used to solve social problems, such as poverty, hunger, and homelessness. The book includes case studies of real-world applications, such as using AI to predict natural disasters and target aid to those who need it most.
Focuses on the implementation aspects of machine learning projects. It covers a variety of topics, including data collection, feature engineering, and model selection.
Focuses on practical AI project implementation, particularly for coders and software developers. Provides a hands-on approach to building and deploying AI models.
Provides a comprehensive guide to AI project implementation using Python. Covers various aspects of AI, including data preparation, model training, and deployment.
Written by the creator of Keras, this book provides a practical guide to deep learning using Python. Covers various aspects of AI project implementation, including model architecture, training techniques, and deployment.
A comprehensive overview of the ways that AI is being used to improve healthcare. The book includes case studies of real-world applications, such as using AI to diagnose disease and develop new treatments.
A passionate call to action for using AI to solve the world's biggest problems. The book includes stories of real-world AI applications that are making a positive impact.
A comprehensive resource guide on the use of AI for social good in developing countries. The book includes case studies of real-world applications, such as using AI to improve agricultural yields and reduce poverty.
Provides a conceptual understanding of AI algorithms and their implementation. Suitable for beginners or those seeking a strong foundation in AI concepts.
A concise introduction to the key concepts of AI and its potential applications for social good. The book is written by a professor at the University of Oxford who leading researcher in the field.
Provides a step-by-step guide to implementing AI projects. It covers a variety of topics, including data collection, model training, and evaluation.
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