Written By Jess Feldman
Edited By Liz Eggleston
Course Report strives to create the most trust-worthy content about coding bootcamps. Read more about Course Report’s Editorial Policy and How We Make Money.
Course Report strives to create the most trust-worthy content about coding bootcamps. Read more about Course Report’s Editorial Policy and How We Make Money.
Jobs in AI are in high demand across many industries, and the responsibilities for these exciting technical roles only continue to evolve. Even with the turbulent tech job market of 2023, LinkedIn's chief economist points to a 21x increase in AI job postings! If you’re interested in the artificial intelligence career path, we’ve rounded up the top 10 AI and machine learning jobs that hiring managers are focusing on in 2024. Learn more about the typical responsibilities and average salaries of AI jobs, and how to pivot your career in artificial intelligence and machine learning through a short course or a bootcamp.
What is an AI job?
Generally speaking, an artificial intelligence (AI) job is one where a person directly works with AI tools or methodologies to create or improve products or systems. These jobs can scale from entry-level roles in AI (such as AI Prompt Engineer and Junior AI Researcher) to more technically complicated roles in AI (such as AI Engineer and AI Architect). As with Data Science jobs, employers may look for PhDs when hiring for mid-level or senior AI roles, so applicants without PhDs will need to prove their experience.
What types of companies hire for AI roles?
At this point, AI is being incorporated into many companies, so you could be in an AI role across all kinds of industries. That said, companies that have been most aggressively hiring for AI roles include tech, finance and insurance, healthcare, education services, and real estate. Coding bootcampers have landed jobs working with AI at companies like JPMorgan Chase, IBM, Salesforce, Labelbox, Notable, and Datacor.
Some of the largest AI companies in 2024 include:
These AI professionals attended bootcamps and have since landed jobs working in or with AI:
Artificial intelligence work continues to evolve, but we’ve heard from the frontlines from a few bootcamp graduates about what working with AI looks like for them:
Varun, a Senior AI Engineer and Flatiron School graduate, says “It is my job to use different models to eventually generate something useful, from a natural language prompt. If I break it down further it looks like this:
Mikiko, a Springboard graduate and the Head of AI Developer Relations at Labelbox, describes her day-to-day in AI like this: "Some weeks I focus a lot more on content, like this week I'm finishing up writing some technical blog posts. I also have to work on an MLOps keynote for a community conference called Data Day Texas, helping to level-set people what they should expect in 2024 in MLOps. I talk to data scientists, ML engineers, and data engineers on various kinds of social platforms to understand what's resonating with them."
Luke, a ML/AI Researcher & Engineer at JPMorgan Chase, describes his job: “Since I’m on the retail branch and more toward the marketing side at Chase, we want to know more about customer behavior. Machine learning is effective at removing repetition. You don’t need to hire a dedicated customer associate to monitor what people do. You can use algorithms to capture those signals and make sure we interact with our customers at the right time. We have better technologies than they did in 2015. We can use neural networks (images, sounds, etc) and AI to interpret the results in a very human-like way, which opens up a lot of possibilities.”
Lou, the Director of Data Science at Datacor says “At my last job, we were working on an unsupervised machine learning model to predict failure rates on machines. We used methods like Principle Component Analysis and clustering methods like DB Scan to determine clusters of similar activity. Outliers from those activities would constitute potential anomalous cycles on these machines. This was a pretty novel method and we were able to patent it!”
Nate, a Software Engineer at Notable, is working with the Integrations team: “I’m not directly developing our AI solutions at Notable, but I do work on the Integrations team so a lot of my job is integrating our technology at Noteable with our customers’ healthcare record systems. A lot of what we do is called RPA (Robotic Process Automation), which tells a machine how to do a task like a person would: logging into a website, searching for a login button, etc. It’s a mixture of OCR (Optical Character Recognition) and image recognition software. We have a dedicated AI/ML team that works on those products and it's my job to fit those into our existing data flow. Most of my job is interacting with that AI team, helping debug, bringing issues to them, and integrating their work into our customer solutions. While I’m not directly developing AI, the advent of things like GitHub Copilot and ChatGPT have helped me level up my programming career. “
“At Salesforce, we have a proprietary generative AI called Einstein CoPilot, and current work is integrating that into my area of the product to help out our personas so they can get their work done faster in Salesforce.” Jeff, Lead Designer at Salesforce
“The tools have been so democratized at this point that anyone can go build machine learning projects and products. There's a new kind of tech role called the AI Engineer or AI Developer, which is basically a software engineer that is able to use a lot of the machine learning libraries and tools to build a product. They don't necessarily need to go through that whole data-to-machine learning cycle.” - Springboard Data Science alum Mikiko
If your goal is to become an AI Engineer, then data analysis is a great starting point. As Senior AI Engineer and Flatiron School alum Varun points out, “This field is moving fast and if you are going to be technical you basically need to show that you know enough to be dangerous in whatever task the company is trying to build a solution for.”
To go from Data Analyst to AI/ML Engineer, you will need to level up your skills. As NYC Data Science Academy alum & ML/AI Researcher Luke puts it: “If you’re considering a career in machine learning (ML)/AI, then you need to understand data!” Get comfortable with programming languages, like Python, SQL, and R. You will also need to understand data science fundamentals, like data modeling and big data analysis, which means knowing how to use tools like Apache Spark and Hadoop. Machine learning modeling is also a key element of the AI/ML roles, so make sure you understand large language models (LLMs)!
In addition to learning the technical skills, find a mentor and/or a community (or an AI/ML bootcamp!) so you can get the support and network you need to make the career pivot.
In this list, we’re rounding up the AI roles that are heavily focused on AI, but keep in mind that most tech roles today (such as Software Engineers, Data Analysts, UX Designers, QA Testers, and Cybersecurity Analysts) are using AI in some capacity. You'll notice the AI roles in this table go from entry-level AI jobs to senior-level AI jobs.
Another thing to note is that as AI rapidly evolves, so do the AI jobs and the AI job titles! For example, Flatiron School grad Varun, who is technically a Senior AI Engineer, also has the full job title of “Senior Software Engineer - Python, AI, GIS Mapping.”
AI Job Title | Experience Level | Job Description | Average Salary |
---|---|---|---|
AI Prompt Engineer | Entry-Level |
|
💰 The average AI Prompt Engineer salary is $63K. |
Junior AI/Machine Learning Engineer | Entry-Level |
|
💰 The average Junior AI/ML Engineer salary is $70K. |
AI/ML Engineer (a.k.a. AI Engineer) | Mid-Level |
|
💰 The average AI Engineer salary is $106K. |
AI Researcher (a.k.a. AI Researcher Engineer) | Mid-Level |
|
💰 The average AI Researcher salary is $131K. |
AI Security Specialist | Mid-Level |
|
💰 The average AI Security Specialist salary is $89K. |
AI Integration Specialist | Mid-Level |
|
💰 The average AI Integration Specialist salary is $105K. |
Senior AI Engineer (a.k.a. Principal AI Engineer) | Senior-Level |
|
💰 The average Senior AI Engineer salary is $151K. |
Senior Machine Learning Scientist | Senior-Level |
|
💰 The average Senior Machine Learning Scientist salary is $111K. |
AI Architect | Senior-Level |
|
💰 The average AI Architect salary is $128K. |
AI Developer Evangelist (a.k.a. Developer Relations or "DevRel") | Senior-Level |
|
💰 The average AI Developer Evangelist salary is $186K. |
Whether you’re upskilling or making a career change into tech with the intention of working in AI, there are so many short AI courses and AI/ML bootcamps to choose from. Before enrolling in a program, define what your career goals are for attending the course and the types of roles (or promotions) you’re looking to receive after graduation.
A short course is a great option for people who want to level up their careers with AI skills. Plus, many of the introductory AI courses are perfect for people who are not working in technical fields!
For people who are looking to learn fundamental AI skills, you may want to consider:
For those who are interested in learning the more technical aspects of artificial intelligence, a better fit may be:
If you’re looking to make a career change into artificial intelligence and machine learning, an AI/ML bootcamp may be the right path for you. Keep in mind that many immersive data science programs also cover machine learning and AI in their curriculum!
Bootcamps with AI/ML programs include:
Jess Feldman is an accomplished writer and the Content Manager at Course Report, the leading platform for career changers who are exploring coding bootcamps. With a background in writing, teaching, and social media management, Jess plays a pivotal role in helping Course Report readers make informed decisions about their educational journey.
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