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How do I develop skills for an AI Engineering Career?
I'm currently an HS senior and I have taken several CS courses and have a general understanding of them. I am planning to apply to a linguistic + CS program for a university, but I am still concerned in trying to find opportunities in the field. Any tips on what I should learn or do?
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10 answers
Updated
Ahtesham’s Answer
You're already doing great with your computer science skills and interest in linguistics. AI engineering is right at that sweet spot. To build your skills, start with Python, the main language used in AI. Get familiar with libraries like TensorFlow, PyTorch and scikit-learn. It's also important to brush up on linear algebra, calculus and statistics, as they're key to understanding AI models.
Since you're considering a linguistics and CS program, try out natural language processing (NLP) projects, like creating chatbots or sentiment analyzers. These projects show how AI applies to language and can help you stand out. You can also join online competitions like Kaggle or DrivenData, or contribute to open-source projects on GitHub to gain practical experience.
Keep up with AI research, take free online courses from places like Google AI or Coursera and connect with others through tech clubs or online communities. These steps will help you stay updated and make it easier to find internships when you're in college.
Since you're considering a linguistics and CS program, try out natural language processing (NLP) projects, like creating chatbots or sentiment analyzers. These projects show how AI applies to language and can help you stand out. You can also join online competitions like Kaggle or DrivenData, or contribute to open-source projects on GitHub to gain practical experience.
Keep up with AI research, take free online courses from places like Google AI or Coursera and connect with others through tech clubs or online communities. These steps will help you stay updated and make it easier to find internships when you're in college.
Updated
Doc’s Answer
Michelle the top 5 skills for an AI engineering career should include EXPERIENCE along with proficiency in PROGRAMMING LANGUAGES, MACHINE LEARNING, DATA ANALYTICS and AI SECURITY.
✅ PROGRAMMING LANGUAGES: Proficiency in languages like Python is essential for AI development, data manipulation, and model integration. One of the most fundamental skills in AI is proficiency in programming languages. Basic languages to know include Python, R, and Java, each of which offers extensive libraries and frameworks specifically designed for AI applications. Python’s simplicity and robust ecosystem make it particularly popular with AI professionals. R is useful for statistical analysis and data visualization, while Java is often used in large-scale AI systems due to its scalability and portability.
✅ MACHINE LEARNING: A deep understanding of various machine learning algorithms and deep learning models, which are the core of AI systems, is crucial. Working in AI requires a comprehensive understanding of machine learning (ML) and deep learning (DL). ML encompasses the creation of algorithms that empower computers to learn from data and make predictions, whereas DL centers on neural networks, particularly deep neural networks. Fundamental concepts include supervised learning, unsupervised learning, and reinforcement learning, all of which empower AI systems to identify patterns, classify data, and make decisions with minimal human intervention. Professionals must also master building and training large language models (LLMs), generative AI, and building AI-enabled systems.
✅ DATA HANDLING AND ANALYSIS: AI professionals must handle large volumes of data with efficiency and expertise. They, therefore, must possess exceptional data management and processing skills. These skills are used for cleaning, organizing, and preparing datasets before those sets train AI models. Techniques like data wrangling, preprocessing, and understanding big data tools (e.g., Hadoop, Apache Spark) guarantee that AI models are built using accurate, high-quality data.
✅ AI SECURITY: Every AI engineer should learn about how to maintain strong security and privacy measures as it is an important part as the use of AI bring new type of security vulnerabilities and it is the responsibility of every engineer to maintain the confidentiality, availability and integrity of data and by having the knowledge and understanding of the rules and regulations related to data protection such as General data protection regulation and by implementing secure AI frameworks which coordinates with these regulations. The engineers should also have the knowledge about the different encryption methods and secure the AI models development practices.
✅ LASTLY EXPERIENCE: There are several ways to earn related experience before working as an AI engineer. Gaining experience gives you practical knowledge of how to implement AI solutions in various ways and can help you build a stronger resume. One way to earn relevant experience is through an internship. Many college degree programs require an internship, but even if yours doesn't, completing an internship voluntarily can provide you with experience. Another way to gain experience is through a part-time job. Look for organizations that have AI engineers in need of part-time assistance. While you may not work directly on AI projects, you can still observe how AI engineers work and the type of projects they complete. Building your own AI projects helps you to discover what areas to improve on and how to optimize the development process. You can then use this project to highlight your abilities with AI during a job interview.
Hope this will be helpful Michelle
✅ PROGRAMMING LANGUAGES: Proficiency in languages like Python is essential for AI development, data manipulation, and model integration. One of the most fundamental skills in AI is proficiency in programming languages. Basic languages to know include Python, R, and Java, each of which offers extensive libraries and frameworks specifically designed for AI applications. Python’s simplicity and robust ecosystem make it particularly popular with AI professionals. R is useful for statistical analysis and data visualization, while Java is often used in large-scale AI systems due to its scalability and portability.
✅ MACHINE LEARNING: A deep understanding of various machine learning algorithms and deep learning models, which are the core of AI systems, is crucial. Working in AI requires a comprehensive understanding of machine learning (ML) and deep learning (DL). ML encompasses the creation of algorithms that empower computers to learn from data and make predictions, whereas DL centers on neural networks, particularly deep neural networks. Fundamental concepts include supervised learning, unsupervised learning, and reinforcement learning, all of which empower AI systems to identify patterns, classify data, and make decisions with minimal human intervention. Professionals must also master building and training large language models (LLMs), generative AI, and building AI-enabled systems.
✅ DATA HANDLING AND ANALYSIS: AI professionals must handle large volumes of data with efficiency and expertise. They, therefore, must possess exceptional data management and processing skills. These skills are used for cleaning, organizing, and preparing datasets before those sets train AI models. Techniques like data wrangling, preprocessing, and understanding big data tools (e.g., Hadoop, Apache Spark) guarantee that AI models are built using accurate, high-quality data.
✅ AI SECURITY: Every AI engineer should learn about how to maintain strong security and privacy measures as it is an important part as the use of AI bring new type of security vulnerabilities and it is the responsibility of every engineer to maintain the confidentiality, availability and integrity of data and by having the knowledge and understanding of the rules and regulations related to data protection such as General data protection regulation and by implementing secure AI frameworks which coordinates with these regulations. The engineers should also have the knowledge about the different encryption methods and secure the AI models development practices.
✅ LASTLY EXPERIENCE: There are several ways to earn related experience before working as an AI engineer. Gaining experience gives you practical knowledge of how to implement AI solutions in various ways and can help you build a stronger resume. One way to earn relevant experience is through an internship. Many college degree programs require an internship, but even if yours doesn't, completing an internship voluntarily can provide you with experience. Another way to gain experience is through a part-time job. Look for organizations that have AI engineers in need of part-time assistance. While you may not work directly on AI projects, you can still observe how AI engineers work and the type of projects they complete. Building your own AI projects helps you to discover what areas to improve on and how to optimize the development process. You can then use this project to highlight your abilities with AI during a job interview.
Hope this will be helpful Michelle
Updated
Margaret’s Answer
Learning Python is another good step. You can find online companies like Stellar AI, Outlier, Alignerr, and Data Annotation that have jobs for students to gain experience in prompt engineering, redlining (testing the security and ethical limits of a model), rubric writing (how responses from an AI model are scored and judged for quality) in addition to your programming courses.
Updated
Nicholas’s Answer
Hi Michelle,
These answers are all very good, but I will offer some perspectives I think they are missing in terms of the tangible steps you can take in your endeavor:
First - the absolute best way to keep pace and get ahead is to be actively interested in AI and build things with it on your own, in your own time. Not everyone in the job pool will be doing this - many will be waiting for a project, assignment, or task to be given to them. Your absolute best bet is to actively read about and practice with the tools you want to learn more about.
If you are struggling to find the motivation or inspiration to do this, I would ask yourself why - and if there is something else in your life you DO feel that way about. Don't fight your interests and talents just because you think you need to do something because of the job market/economy/peer pressure. You will be rewarded for your talents wherever you go and trying to force yourself to do something you're not actually interested in will burn you out (ask me how I know!).
As for the 'doing things in your own time' part - this is what I recommend: Think about something small, tangible, and theoretically achievable that you are interested in doing. This could be creating a small web application, or digging into some data you'd like to analyze, really anything - but you should be able to see what success looks like for the project and you should be interested in solving the problem or uncovering the mystery.
Here are some examples I have either done or thought of doing, to give you an idea:
- Using various data sources (cruise ship manifests, Ticketmaster) to create an app for local residents to let them know if downtown restaurants will be crowded
- An AI-enabled notetaking application for D&D players
- A RAG (ai-enabled data retrieval) tool + chatbot for specific domains (in my case, for old fireplaces using the PDFs of their manuals, but this could be anything you like)
- Scraping the websites of my local grocery stores to do automated price comparisons before going shopping
Once you have your idea(s), then go to your favorite Foundation Model LLM and follow these steps:
1) Explain the project you'd like to do in detail
2) Let it know what you know already (which coding languages, domain knowledge, or tools you have, for example) and how you think you might want to tackle the project (which technology, data sources, etc.)
3) Ask it to lay out the project plan for you with step-by-step instructions, having it give you only one or two steps at a time for you to review before moving on to the next step (this will help it to not give you a detailed 16-step plan when you don't know how to do step 3)
4) Continue to work with it to create your master project plan until you feel like you have an understanding of all of the steps you need to take and the things you need to learn
5) Work through the plan and start your project!
Best of luck. If you feel behind - trust me, you are not! This is a nascent environment, and you can (and will) catch up.
These answers are all very good, but I will offer some perspectives I think they are missing in terms of the tangible steps you can take in your endeavor:
First - the absolute best way to keep pace and get ahead is to be actively interested in AI and build things with it on your own, in your own time. Not everyone in the job pool will be doing this - many will be waiting for a project, assignment, or task to be given to them. Your absolute best bet is to actively read about and practice with the tools you want to learn more about.
If you are struggling to find the motivation or inspiration to do this, I would ask yourself why - and if there is something else in your life you DO feel that way about. Don't fight your interests and talents just because you think you need to do something because of the job market/economy/peer pressure. You will be rewarded for your talents wherever you go and trying to force yourself to do something you're not actually interested in will burn you out (ask me how I know!).
As for the 'doing things in your own time' part - this is what I recommend: Think about something small, tangible, and theoretically achievable that you are interested in doing. This could be creating a small web application, or digging into some data you'd like to analyze, really anything - but you should be able to see what success looks like for the project and you should be interested in solving the problem or uncovering the mystery.
Here are some examples I have either done or thought of doing, to give you an idea:
- Using various data sources (cruise ship manifests, Ticketmaster) to create an app for local residents to let them know if downtown restaurants will be crowded
- An AI-enabled notetaking application for D&D players
- A RAG (ai-enabled data retrieval) tool + chatbot for specific domains (in my case, for old fireplaces using the PDFs of their manuals, but this could be anything you like)
- Scraping the websites of my local grocery stores to do automated price comparisons before going shopping
Once you have your idea(s), then go to your favorite Foundation Model LLM and follow these steps:
1) Explain the project you'd like to do in detail
2) Let it know what you know already (which coding languages, domain knowledge, or tools you have, for example) and how you think you might want to tackle the project (which technology, data sources, etc.)
3) Ask it to lay out the project plan for you with step-by-step instructions, having it give you only one or two steps at a time for you to review before moving on to the next step (this will help it to not give you a detailed 16-step plan when you don't know how to do step 3)
4) Continue to work with it to create your master project plan until you feel like you have an understanding of all of the steps you need to take and the things you need to learn
5) Work through the plan and start your project!
Best of luck. If you feel behind - trust me, you are not! This is a nascent environment, and you can (and will) catch up.
Updated
Don’s Answer
Hello Michelle,
There are many different ways to develop your skills when it comes to AI engineering. Here is a list of things for you to improve on, look into or things you need to learn.
List:
-Master your core technical skills that are needed
-Improve on your knowledge of Math when it comes to things like statistics and linear algebra
-Understanding different program languages like Python and more
-Understand how to manage different types of data needed
-Start getting experience when it comes to beginner projects
Ultimately depending on what type of AI engineer you want to be there will be added requirements, experience and knowledge of different programs needed. I would say explore ways you can get started, see if there are different universities/colleges/jobs that provide training and keep on learning. Best of luck to you!
There are many different ways to develop your skills when it comes to AI engineering. Here is a list of things for you to improve on, look into or things you need to learn.
List:
-Master your core technical skills that are needed
-Improve on your knowledge of Math when it comes to things like statistics and linear algebra
-Understanding different program languages like Python and more
-Understand how to manage different types of data needed
-Start getting experience when it comes to beginner projects
Ultimately depending on what type of AI engineer you want to be there will be added requirements, experience and knowledge of different programs needed. I would say explore ways you can get started, see if there are different universities/colleges/jobs that provide training and keep on learning. Best of luck to you!
Updated
Emily’s Answer
All the answers I've seen so far have focused on the technical side, but I add that you shouldn't neglect your soft skills. AI will certainly change the way we work, but there are certain skills that will always be relevant. Examples:
- Tackling an abstract problem where you have little experience
- Being able to communicate well and effectively
- Working in a team
- Being likeable enough that people want to work with you
While I can't answer how AI will change the world, I still think we will work with other people because it is human nature to want to belong to communities. Having the soft skills to exist in a community will always be valuable.
- Tackling an abstract problem where you have little experience
- Being able to communicate well and effectively
- Working in a team
- Being likeable enough that people want to work with you
While I can't answer how AI will change the world, I still think we will work with other people because it is human nature to want to belong to communities. Having the soft skills to exist in a community will always be valuable.
Andrew Boyd II
Technical Program Manager for Azure Cloud
4
Answers
Updated
Andrew’s Answer
Hi Michelle!
People have given some great advice here. I would recommend doing two things:
1. Learn key concepts
I suggest learning some of the basics about AI, AI Agents, and Model Context Protocol (MCP). You can take this guided course here to begin learning and earn a badge! Make sure to select the option where you can get credits for free: https://learn.microsoft.com/en-us/training/courses/ai-3026
2. Build a project
Once you've done this course, I suggest turning an idea you have into an app. This is very easy and quick to do, as you can use vibe coding solutions such as Github Copilot, Loveable.dev, Cursor, or other tools to turn written prompts into actual apps.
Learning the key concepts, building a project, and putting this on your resume will be a great start to starting your AI journey and making yourself a top candidate for AI-related jobs.
People have given some great advice here. I would recommend doing two things:
1. Learn key concepts
I suggest learning some of the basics about AI, AI Agents, and Model Context Protocol (MCP). You can take this guided course here to begin learning and earn a badge! Make sure to select the option where you can get credits for free: https://learn.microsoft.com/en-us/training/courses/ai-3026
2. Build a project
Once you've done this course, I suggest turning an idea you have into an app. This is very easy and quick to do, as you can use vibe coding solutions such as Github Copilot, Loveable.dev, Cursor, or other tools to turn written prompts into actual apps.
Learning the key concepts, building a project, and putting this on your resume will be a great start to starting your AI journey and making yourself a top candidate for AI-related jobs.
Updated
Jeffrey’s Answer
Hi Michelle,
Let's try to project 5 yrs out for a career leveraging AI realizing that no one has any idea of what jobs will be available and viable given the accelerating pace that AI is being deployed. The guidance I provide below is predicated on the belief that humans in the loop and on the loop will still be very relevant for the foreseeable future. That being said, you will need to be flexible and adaptable and constantly learning and leveraging technology.
Below are 5 key points (building on Doc's earlier response):
(1) Build fluency with programming languages commonly used in AI (Python, R, Java, etc.) and gain experience with major AI/ML platforms and libraries.
(2) Deepen your understanding of linguistics—especially computational linguistics—by enrolling in courses and pursuing related research or projects.
(3) Stay updated on trends in AI through reputable sources like academic journals, expert blogs, and news outlets focused on technology. (See list ONE below)
(4) Foster friendships and networks with diverse peers in both CS and linguistics programs to broaden perspectives.
(5) Consider joining university clubs and professional societies related to AI, linguistics, and technology to expand opportunities and mentorship (see list TWO below)
List ONE:
==============================
AI & CS
- Foundations and Trends in Machine Learning
- Journal of Machine Learning Research
- Nature Machine Intelligence
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Artificial Intelligence Review
- Science Robotics
- International Journal of Information Management
- Neural Information Processing Systems (NeurIPS—conference proceedings often published)
Linguistics & Computational Linguistics
- Computational Linguistics (MIT Press)
- Journal of Language Modelling
- Natural Language Engineering
- Transactions of the Association for Computational Linguistics (TACL)
- Computer Speech & Language
Expert Blogs
- MarkTechPost
- NVIDIA Blog
- Google AI Blog
List TWO
=======================
Here are 3 programs tailored to high school/pre-university students which will provide support and growth opportunities before and during transition to university studies.
1. Inspirit AI - offers research mentorship programs specifically designed for high school students interested in AI and provides hands-on projects, small group mentorship, and guidance from top university students and industry professionals. This is a GREAT entry point to build fundamental AI skills and cultivate professional connections early.
2. Association for Computational Linguistics (ACL) Mentorship - offers a dedicated mentorship program for students, including year-round resources and direct pairing with mentors in computational linguistics and natural language processing. They also provide access to a global community, mentorship events, conferences, and workshops tailored to students, which is especially valuable if you are interested in the intersection of AI and language & knowledge.
3. AI4ALL - focuses on high schoolers and undergraduates, especially from underrepresented backgrounds, with summer programs and year-round outreach in AI and provides opportunities for hands-on learning, mentorship from AI leaders, and networking with peers across the country.
Jeffrey
Let's try to project 5 yrs out for a career leveraging AI realizing that no one has any idea of what jobs will be available and viable given the accelerating pace that AI is being deployed. The guidance I provide below is predicated on the belief that humans in the loop and on the loop will still be very relevant for the foreseeable future. That being said, you will need to be flexible and adaptable and constantly learning and leveraging technology.
Below are 5 key points (building on Doc's earlier response):
(1) Build fluency with programming languages commonly used in AI (Python, R, Java, etc.) and gain experience with major AI/ML platforms and libraries.
(2) Deepen your understanding of linguistics—especially computational linguistics—by enrolling in courses and pursuing related research or projects.
(3) Stay updated on trends in AI through reputable sources like academic journals, expert blogs, and news outlets focused on technology. (See list ONE below)
(4) Foster friendships and networks with diverse peers in both CS and linguistics programs to broaden perspectives.
(5) Consider joining university clubs and professional societies related to AI, linguistics, and technology to expand opportunities and mentorship (see list TWO below)
List ONE:
==============================
AI & CS
- Foundations and Trends in Machine Learning
- Journal of Machine Learning Research
- Nature Machine Intelligence
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Artificial Intelligence Review
- Science Robotics
- International Journal of Information Management
- Neural Information Processing Systems (NeurIPS—conference proceedings often published)
Linguistics & Computational Linguistics
- Computational Linguistics (MIT Press)
- Journal of Language Modelling
- Natural Language Engineering
- Transactions of the Association for Computational Linguistics (TACL)
- Computer Speech & Language
Expert Blogs
- MarkTechPost
- NVIDIA Blog
- Google AI Blog
List TWO
=======================
Here are 3 programs tailored to high school/pre-university students which will provide support and growth opportunities before and during transition to university studies.
1. Inspirit AI - offers research mentorship programs specifically designed for high school students interested in AI and provides hands-on projects, small group mentorship, and guidance from top university students and industry professionals. This is a GREAT entry point to build fundamental AI skills and cultivate professional connections early.
2. Association for Computational Linguistics (ACL) Mentorship - offers a dedicated mentorship program for students, including year-round resources and direct pairing with mentors in computational linguistics and natural language processing. They also provide access to a global community, mentorship events, conferences, and workshops tailored to students, which is especially valuable if you are interested in the intersection of AI and language & knowledge.
3. AI4ALL - focuses on high schoolers and undergraduates, especially from underrepresented backgrounds, with summer programs and year-round outreach in AI and provides opportunities for hands-on learning, mentorship from AI leaders, and networking with peers across the country.
Jeffrey
Updated
Michael’s Answer
Embrace Vibe Coding. Many tools like GitHub Copilot, Cursor, Lovable, ChatGPT, and Claude can help you write code even if you have little coding experience. Using these tools will teach you valuable skills. As you get better at using them, you'll become more appealing to tech employers. More companies want people who have experience with AI engineering tools. You can easily start learning through resources like YouTube, podcasts and blog posts.
Do some research on the term Vibe Coding using Google or ChatGPT
Do some brainstorming about an app or tool that you want to code
Pick a tool to try out such as Cursor, Claude, Lovable
Try out vibe coding. If you get stuck, use the tool or ChatGPT to ask questions. They are typically very good in meeting you where you skill level is at and help you sort things out.
Michael recommends the following next steps:
Updated
vignesh’s Answer
Begin by mastering the basics—Python, math (like algebra, calculus, and statistics), and the essentials of machine learning. Next, dive into creating projects using AI tools and frameworks such as TensorFlow, PyTorch, or OpenAI APIs. These projects will give you valuable real-world experience.