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skills needed to work in ai?
after looking you need a mix of technical expertise
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5 answers
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Liam’s Answer
Linux/ bash, python, security basics, networking basics, an analytical mind.
This is a really off the top answer and most people don't have a professional background in this field. Working in AI can be anything from chip design and fabrication, to ethical and philosophical implementation.
If you want to understand how AI exists, make a smallish homelab hosting your own AI, then compare your prompts on it and a free version of ChatGPT (Gemini, Claude, Perplexity etc.) and see how your prompts work. Using a low power computer to do prompting will allow you to see how the hardware and the models work together.
If you really want to master AI, learn Linux inside and out, learn python/ matlab/ bash as well as an OOP language, chip and networking architecture, prompt engineering, and computing fundamentals. Also start to read and understand industrial and management systems, read philosophy, read literature (maybe some sci-fi?), scientific publications, and some publications based on ethics. I know I basically just told you to get a PhD in AI but this is high level and the larger skill set that is required to work making and maintaining AI.
At a lower level, get a free account, a free class, and learn prompt engineering. Make a prompt library and learn note taking so you can summon the skills you have created when you need them. I feel like the real skill overall is to be able to recall what you have learned and using AI as an organizational system is just the glue in the binding of the book you make.
You should strive to be able to use AI as a professional skill no matter what, but only get to the higher levels if you really wish to pursue it as a career.
https://www.youtube.com/@unsupervised-learning
https://www.coursera.org/learn/advanced-prompt-engineering-for-everyone
https://www.youtube.com/@Computerphile
This is a really off the top answer and most people don't have a professional background in this field. Working in AI can be anything from chip design and fabrication, to ethical and philosophical implementation.
If you want to understand how AI exists, make a smallish homelab hosting your own AI, then compare your prompts on it and a free version of ChatGPT (Gemini, Claude, Perplexity etc.) and see how your prompts work. Using a low power computer to do prompting will allow you to see how the hardware and the models work together.
If you really want to master AI, learn Linux inside and out, learn python/ matlab/ bash as well as an OOP language, chip and networking architecture, prompt engineering, and computing fundamentals. Also start to read and understand industrial and management systems, read philosophy, read literature (maybe some sci-fi?), scientific publications, and some publications based on ethics. I know I basically just told you to get a PhD in AI but this is high level and the larger skill set that is required to work making and maintaining AI.
At a lower level, get a free account, a free class, and learn prompt engineering. Make a prompt library and learn note taking so you can summon the skills you have created when you need them. I feel like the real skill overall is to be able to recall what you have learned and using AI as an organizational system is just the glue in the binding of the book you make.
You should strive to be able to use AI as a professional skill no matter what, but only get to the higher levels if you really wish to pursue it as a career.
Liam recommends the following next steps:
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Neli’s Answer
Prompt engineering and machine learning are a must in my opinion.
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Mari’s Answer
To succeed in AI, you need both technical and soft skills. Focus on learning Python, SQL, machine learning, prompt engineering, data handling, and basic cloud and deployment knowledge. Also, work on your communication, critical thinking, adaptability, and ability to explain AI results clearly.
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Manasa’s Answer
1. Understanding the basics of AI and programming is essential. You should be able to read code, grasp its purpose, and assess its efficiency.
2. Mastering prompt engineering is crucial. Learn to ask the right questions to generate code, brainstorm, or handle any software development task effectively.
3. If you can, choose a specific field like insurance or banking or something else to specialize in. This focus will set you apart, and while job options may be fewer than general coding, your chances of getting hired quickly will improve.
2. Mastering prompt engineering is crucial. Learn to ask the right questions to generate code, brainstorm, or handle any software development task effectively.
3. If you can, choose a specific field like insurance or banking or something else to specialize in. This focus will set you apart, and while job options may be fewer than general coding, your chances of getting hired quickly will improve.
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PARTH’s Answer
To work in AI, you need a mix of technical, data, and practical delivery skills:
Programming: Python first, plus SQL for working with data; Git is also table stakes.
ML fundamentals: supervised/unsupervised learning, model training, evaluation, and basics of deep learning.
Data skills: cleaning data, feature engineering, analysis, and understanding pipelines/ETL.
GenAI skills: prompt engineering, LLM basics, RAG, and tools/frameworks like LangChain.
Deployment skills: cloud, APIs, Docker, MLOps, and monitoring models in production.
Soft skills: problem-solving, communication, business understanding, and responsible AI awareness matter a lot in hiring.
Best practical advice: start with Python + SQL + ML basics + 2 real projects. Employers increasingly value hands-on projects more than just theory, so build something usable, not just notebooks.
Programming: Python first, plus SQL for working with data; Git is also table stakes.
ML fundamentals: supervised/unsupervised learning, model training, evaluation, and basics of deep learning.
Data skills: cleaning data, feature engineering, analysis, and understanding pipelines/ETL.
GenAI skills: prompt engineering, LLM basics, RAG, and tools/frameworks like LangChain.
Deployment skills: cloud, APIs, Docker, MLOps, and monitoring models in production.
Soft skills: problem-solving, communication, business understanding, and responsible AI awareness matter a lot in hiring.
Best practical advice: start with Python + SQL + ML basics + 2 real projects. Employers increasingly value hands-on projects more than just theory, so build something usable, not just notebooks.