It’s an exciting time for artificial intelligence (AI). We’re still a long way from the sentient robots you see in movies like Ex Machina and The Terminator, but with the rise of large language models and scarily-impressive programs like ChatGPT and DALL-E, the question on everyone’s mind is: Will AI replace our jobs?
Experts say: Probably not. As generative AI tools become more sophisticated, so will the ways we interact with them. If you understand the technology, there are tons of opportunities to use these tools to your advantage as a developer. (Check out our free course Intro to ChatGPT to learn how.)
Plus, surveys suggest that this advancement in AI is creating jobs — and you could have the skills you need to get hired. Companies worldwide are looking for AI and machine learning experts to help them find ways to use cutting-edge tech to improve their products and operations, and the demand is skyrocketing for many of the roles we’re about to explore.
Learn ChatGPT for free
AI Engineers build AI solutions to complex problems. Their responsibilities can range from building chatbots and smart assistants with natural language processing (NLP) to developing internal algorithms and programs that help automate a company’s processes.
An AI Engineer’s tools will depend on their specific role and specialization, but generally, the role requires strong programming, data science, and math skills. Python is one of the most popular programming languages used for machine learning and AI, and it’s a great place to start if you want to get into the field. You can learn the basics of the language in our Learn Python course.If you want to learn everything you need to land an entry-level job in AI, check out our Machine Learning/AI Engineer career path.
This list includes a lot of tech-heavy roles, but you don’t need to be a programmer to work with AI. Being really good at writing prompts for chatbots is an in-demand skill to have on your resume if you want to become a Prompt Engineer (and you can learn how to write an effective prompt in our Intro to ChatGPT).
AI needs to understand its users, which is no easy task considering the ambiguities of human communication. The way we ask ChatGPT for information can affect the types of responses we get. Prompt Engineers figure out exactly how to word a command to achieve a desired result, and they help evaluate AI performance and uncover flaws by testing models with specialized and specific prompts. These tests can range from complex requests like essays on complicated subjects, to shorter prompts with subtle differences that help assess how word choice influences results.
Prompt engineering helps ensure that AI can properly interpret and respond to our commands, and companies will doubtlessly need native speakers of different languages and dialects worldwide to help train their models.
As a burgeoning career path, there’s no set roadmap to prompt engineering yet. The writing-heavy role requires strong communication skills, and a familiarity with AI systems and NLP is a plus.
Machine Learning Engineer
Machine Learning Engineers teach computers how to use data to make predictions, and they help build tools like recommender engines and facial recognition software. Many use Python and machine learning libraries like TensorFlow and Pandas to build and fine-tune their systems, but they also need strong data analysis and management skills to work with the huge datasets that train their models.
If you already know a little Python, check out our course Intro to Machine Learning (free for a limited time) to learn how to use it for machine learning. And if you want to build the skills you’ll need for a job, try our Machine Learning Engineer career path.
Algorithms underlie an AI’s ability to learn from data, and algorithm engineering requires extensive knowledge of computer science and architecture, data structures, programming, and development. Algorithm Engineers build and fine-tune algorithms for machine learning and AI systems and applications, and while the tools they use will depend on the projects they work on, Java and C++ are used extensively in the field.
If you want to start building the skills you need to become an Algorithm Engineer, try our course Learn Data Structures and Algorithms with Python.
Big Data Engineer
Raw data has to be prepped before it can be used, and Data Engineers build pipelines that automatically collect, clean, and format data for analysis. Big Data Engineers do the same thing, but on a much larger scale. (Big data refers to a dataset that’s so big it’s impossible to store, process, or analyze using traditional data science methods.) Their primary tools are SQL, a programming language used to query databases, and data management frameworks like Apache Spark or Hadoop to extract, process, query, and transform data at scale.
Want to learn more about big data engineering? Check out our free course Introduction to Big Data with PySpark.
NLP sits at the heart of human-computer interaction, and NLP Engineers build tools and systems for parsing and processing text and language. While the most common NLP tools include virtual assistants like Siri and Alexa, NLP is also used in search engines, email filters, and recommender systems.
If you know a little bit of Python and want to dip your toes into NLP, check out our course Learn Text Generation (you can take this for free until April 17) or our skill path Apply Natural Language Processing With Python.
“Data Scientist” is a catch-all that encompasses many of the roles listed above (and many others). While there are several different kinds of Data Scientists, many of them build machine learning models, algorithms, and applications. Others may help build the pipelines that collect and prepare training data.
R is one of the most popular programming languages among Data Scientists. It was designed for statistics, and it has many applications in machine learning and AI. You can pick up the basics in our free course Learn R, then start building your data science skills with our course Learn Linear Regression in R (free for a limited time) and our Data Scientist: Machine Learning Specialist career path.
How to start a career in AI
Tons of tech companies have dropped degree requirements for various roles, so you no longer need a degree to find a job in AI. Many people are building their skills and launching new careers on their own.
If a career in AI sounds right for you, you can start building your skills with the courses below (they’re free until April 17).
- Intro to Machine Learning
- Intro to Cloud Computing
- Learn Linear Regression with R
- Learn Text Generation
Then once you’ve sharpened your skills, you’ll need a portfolio of coding projects that showcase your skills with machine learning and AI. Our projects library is a great source of info if you need ideas, and we also offer Career Services that’ll help you learn about and prepare for the interview process.