How to be a decent AI project manager in Taiwan?

AI Discussions
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Hi everyone,
I’m currently an IT Project Manager (ITPM) at DBS with over 10 years of experience in the software industry, strong project management skills, IT knowledge, and fluency in English. While I have a solid background in managing software development and IT projects, I have not yet worked on projects specifically in AI.

I’m looking to pivot my career toward AI project management roles, particularly in Los Angeles, and would love to get advice from this community on how to best build up domain knowledge in AI. What are the key skills and resources I should focus on to effectively transition into AI project management? What technical or soft skills are most valued in AI-related PM roles? Any recommendations for learning paths, certifications, or ways to gain practical experience?

Thanks in advance for your guidance!

Best regards,
Allen

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I believe many best practices in project management transfer directly. The process of listening to requirements and mapping to a time bounded plan for capability delivery while identifying, mitigating and retiring risk is the same. Managing people resources and their personalities is the same. What is different is understanding of what is hard and what is easy, how risky some tasks are, and producing time estimates. I would advise learn about not just what comprises AI-based solutions, but using AI-based tools to help develop them. If I were looking to hire a project manager for an AI solution I would hope they were well grounded in relevant machine learning foundations - supervised and unsupervised learning, data cleansing and normalization and probably enough modern NLP to grasp conceptually how Large Language Models do what they do. To me, the more you understand about the mathematical underpinnings the less it seems like magic and the more reliable your planning and steering control will be.

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Good Morning AI_Curious,

Thank you for the detailed guidance, much appreciate it.
I started this course to understand AI concepts better - ai-for-everyone.

I will prepare myself to gain hands-on experience with the following tools:
Open-source frameworks
ML Frameworks:

  1. PyTorch
  2. TensorFlow
  3. Hugging Face
  4. PaddlePaddle
  5. Scikit-learn
  6. R

Research publications

  1. Arxiv

Open-source repositories

  1. GitHub
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It occurs to me as I look at your sensible list of learning areas that I should have mentioned that the DevOps cycle, especially post-initial deployment, can be different for ML/AI solutions due to the dependencies on data and (re)training. A truly successful project doesn’t end with passing an Initial Operational Acceptance test and throwing a binary over a wall. It’s a comparatively young field, but don’t neglect adding some AI DevOps to your knowledge kit.

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