Artificial Intelligence Jobs + Career Path for Bootcampers

Table of Contents

• AI Jobs 101
• 10 AI Job Titles in 2024
• How to Learn AI Skills

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.

AI Jobs 101

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:

1. Google - Google has been investing in AI for many years, and recently announced their LLM, Gemini by Google Deepmind.
2. IBM - IBM has created the data and AI platform, IBM watsonx, which offers a series of AI assistants for businesses.  
3. Amazon - Amazon Web Services (AWS) has released Amazon CodeWhisperer, an AI-powered productivity tool for command line. 
4. OpenAI - OpenAI released the generative AI tool, ChatGPT in 2022, and it continues to evolve.
5. Meta - Meta AI created the large language model, LLaMA (Large Language Model Meta AI), which takes a sequence of words and predicts the next word and generates text.  
6. NVIDIA - NVIDIA is a tech company that designs and sells GPUs for cryptocurrency, gaming, and more and also creates chips systems for vehicles, robots, and other tools.
7. AlphaSense - AlphaSense is a market intelligence and search platform for biopharma and healthcare.
8. People.ai - People.ai is an AI-powered sales revenue platform that provides marketing and sales teams with insights.

What It Actually Looks Like to Work in AI

These AI professionals attended bootcamps and have since landed jobs working in or with AI:

• Flatiron School alum Varun now works as a Senior AI Engineer.
• Springboard alum Mikiko now works as the Head of AI Developer Relations.
• NYC Data Science Academy alum Luke now works as an ML/AI Researcher.

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:

• Research: Stay updated with the latest advancements in the field. This could involve reading research papers, attending seminars or webinars, and participating in online forums and communities. This is crucial as the field of AI and machine learning is evolving rapidly.
• Data Preparation: Work on preparing and pre-processing the data for training language models. This involves collecting data, cleaning it, and converting it into a format that can be used for machine learning.
• Model Development and Training: Design and implement machine learning models. This includes choosing the right algorithms, tuning parameters, and training the model on the prepared data. This process often requires running experiments and making iterative improvements based on the results. Many times, I am building on pre-trained models with either fine tuning, or instruction via prompts.
• Model Evaluation: Evaluate the performance of the models using appropriate metrics. This often involves testing the model on a held-out validation set and analyzing the results.
• Collaboration: Work closely with other teams, such as product development, to integrate the AI models into products or services. This could involve optimizing the model for deployment, working on the user interface, or addressing user feedback.
• Documentation and Presentation: Document the work for future reference and present findings to stakeholders or to the technical team. This might involve writing technical reports, creating presentations, or showing working code.”

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

How to go from Data Analyst to AI/ML Engineer

“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.  

10 AI Job Titles in 2024

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.”

How to Learn AI Skills

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.

Artificial Intelligence Short Courses 

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:

• General Assembly’s 1-Hour Intro to Generative AI Course
• Eleven Fifty Academy’s introductory AI courses
• DigitalCrafts’ Artificial Intelligence Fundamentals Certificate program 

For those who are interested in learning the more technical aspects of artificial intelligence, a better fit may be: 

• General Assembly’s Applied Generative AI Workshop
• DigitalCrafts’ AI & Machine Learning Engineering Certificate program
• BrainStation’s Artificial Intelligence Course 

Artificial Intelligence/Machine Learning Bootcamps

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:

• NYC Data Science Academy
• Fullstack Academy
• Clarusway
• Turing College