Artificial Intelligence (AI) is a booming field reshaping industries and creating exciting job opportunities.
From self-driving cars to AI chatbots that can write jokes, AI has become part of daily life—for better or worse.
The good news for job seekers: artificial intelligence jobs are on the rise. In the U.S., AI-related jobs are among the fastest-growing positions, and they are projected to grow 21% by 2031.
Globally, demand for AI specialists has skyrocketed, growing 450% since 2013, with another 40% growth expected by 2025
But how do you get started in such a cutting-edge field?
What kinds of AI jobs are out there, and do you need to be a coding genius? This article will guide you through the AI career landscape.
There 's something for everyone. From your entry-level roles (including part-time gigs and internships) to mid-level positions and senior management paths, artificial intelligence jobs run the entire gamut.
We’ll also look at why some AI jobs are contract-based or part-time and how those can be stepping stones to full-time success. Whether you’re a new graduate, a professional with some tech experience, or even someone from a non-technical background, there’s a path for you in the world of AI.
Let’s dive in with an optimistic outlook (and a dash of lighthearted insight) into launching your AI career!
AI isn’t just a tech trend, it’s a global shift.
For those of us who remember the dot com era or the advent of the internet, we know times like these can feel unsettling. We'd challenge you to change your mindset; to see this "advent" of AI as a huge possibility.
Industries worldwide are investing in AI, from healthcare using machine learning to improve diagnoses, to finance using AI for fraud detection, to entertainment using AI for personalized recommendations.
This widespread adoption means lots of new jobs. In fact, many of the “jobs of the future” will be in AI and data. The World Economic Forum estimates that about 170 million new jobs will be created this decade, many in tech and AI-driven roles
In the United States, companies are racing to hire AI talent. Tech giants and startups alike are building AI teams, and even traditional companies (think banks, retailers, hospitals) need AI experts to stay competitive. This high demand has led to a bit of a talent shortage, especially when it comes to niche skilled positions.
As a result, AI specialists (like data scientists and machine learning engineers) are in the driver’s seat with plenty of opportunities. Salaries can be highly attractive (some senior AI experts even command six-figure salaries early in their careers), and job openings often outnumber applicants.
Global trends also shape the AI job market.
For example, when local companies can’t find AI experts nearby, they look globally to build their AI teams. It’s common now for a U.S. company to hire an AI engineer in Canada or India remotely. Companies are tapping into worldwide talent pools and hiring international contractors when local talent is scarce.
AI research and development is happening on every continent – the U.S. and China are leaders, but you’ll also find AI innovation hubs in Europe, Canada, India, and beyond. For you, as an aspiring AI professional, this global boom means more options: you might work for an overseas client from your home, or join a multicultural team solving global problems via AI.
The takeaway: AI is everywhere, and the opportunities are vast and growing.
One unique aspect of the AI field is the mix of full-time positions and contract or part-time gigs.
You might notice job postings for full-time machine learning engineers at a big company, and other postings for contract AI consultants or part-time data analysts. Why are some roles contract-based and others full-time? Let’s break it down:
Many AI projects are experimental or short-term. A company might need an expert to develop a prototype model or analyze a specific dataset.
In scenarios like this, hiring a contractor or consultant can make sense. Data scientists and machine learning engineers often work as independent contractors when projects are temporary. For example, a small business might contract an AI developer to implement a one-time solution (like a recommendation engine).
TL;DR: Once the project is done, the contract ends.
On the other hand, if an organization is building AI into its core products or services, they’ll want full-time employees.
Roles like AI research scientist in a lab or an in-house machine learning engineer for a product team usually require ongoing work—refining models, maintaining AI systems, and collaborating with other departments continuously.
These are marathon roles, not sprints. A self-driving car company, for instance, needs its AI team on board full-time to constantly improve the driving algorithm.
Companies also use contract roles to stay flexible. In a fast-moving field like AI, a company might not be sure it needs a certain role permanently, so they try a contractor first.
With an ongoing shortage of AI talent, hiring full-time can be tough and time-consuming.
Contracting allows companies to onboard talent quickly for pressing needs.
The rise of remote work and freelancing has reached AI too. Many professionals enjoy contracting because it lets them work on varied projects or have a flexible schedule.
Platforms like Upwork report a surge in freelance AI work.
Demand for skills like generative AI modeling and AI data annotation has grown by over 200% year-on-year on freelance marketplaces.
What does this mean for jobseekers? If you're looking to build experience, there’s a growing market for short-term AI gigs.
Common Contract or Part-Time Roles
Common Full-Time AI Roles
Here at HireArt, we are trying to ditch the antiquated notion that full-time is "better" than contract or part-time. The workforce is changing. Neither path is “better” than the other. Full-time and contract positions often complement each other in the AI ecosystem.
In fact, many AI professionals do a mix: perhaps starting with contract gigs to build experience, then moving into a full-time role once they find their niche. (We love the contract to hire route.)
Remember, every project you complete and every connection you make can pave the way to something bigger. A contract role might be short, but the experience (and references) you gain are lasting.
Many AI professionals look back and say, “That 6-month contract job was what launched my career.” So don’t be afraid to start small or temporary. It can lead to the (unexpected)full-time AI career of your dreams.
So you’re ready to jump in at the ground floor. What entry-level AI roles should you look for?
Whether you’re a new graduate or a career changer freshly equipped with AI courses, here are some common entry-level titles and tips on these roles:
Many organizations offer AI internships – roles where you support a team of experienced AI engineers or data scientists. You might help with data preprocessing, testing models, or documentation. It’s a learning-heavy role (you get mentored) and a great way to apply academic knowledge to real projects.
Pro Tip: Apply broadly for internships, including at non-tech companies that have AI labs or innovation teams.
These are full-time junior positions where you work on parts of AI projects under guidance. As a junior data scientist, you might handle tasks like exploring datasets, creating simple models, or generating reports. A junior machine learning engineer might focus on coding modules of a larger ML pipeline or tweaking algorithms that seniors design.
Skill requirements: Usually a good foundation in Python/R, knowledge of machine learning basics, and some portfolio projects or research experience.
Data analyst roles typically involve analyzing data to derive insights (using tools like SQL, Excel, Python). While not all data analyst jobs are “AI,” many are starting to incorporate AI tools. If you land an analyst job, you can volunteer to work on or learn about any AI initiatives the company has. This role builds your understanding of data – the fuel for all AI.
Career path: Data analysts can evolve into data scientists by learning more AI techniques on the job.
Some companies hire entry-level developers specifically to work on AI projects. This might be titled “Associate AI Engineer” or “Jr. AI Developer.” You’ll be writing code for AI applications, which could range from integrating an AI API into an app to writing Python scripts for model training.
Expect to: spend a lot of time debugging and improving code, and learning software engineering best practices for AI (like how to handle big data or deploy models).
If you’re interested in the cutting-edge and perhaps considering a future PhD, a research assistant role at an AI lab (in a university or a company’s R&D division) can be ideal.
You assist researchers in experiments – maybe training neural networks, doing literature reviews, or running simulations. It’s often term-based (e.g. a year-long contract) and can sometimes be part-time.
Benefit: You’ll get exposure to advanced AI theory and can co-author papers, giving you strong credentials.
This is often a contract or part-time entry role where you label training data (like tagging images, transcribing audio for speech models, etc.). It’s a foot-in-the-door for AI because you learn how AI models are built from the ground up. While it’s more on the routine side, doing this job can help you transition to QA testing of models or data engineering roles.
Quirky Benefit: You’ll become very familiar with the data quirks that AI models have to handle.
When it comes to launching (or relaunching) an AI career, don’t worry if your first job isn’t your dream role.
Think of it like a launchpad. Every experience will teach you something and open new doors.
Once you have a few years of experience or a solid skill foundation, you move into mid-level AI roles. At this stage, you’re expected to handle projects more independently, perhaps even mentor juniors, and contribute ideas to AI solutions. Here are key mid-level positions and what they entail:
By mid-level, data scientists design and deploy their own models. You might own an entire project (say, developing a customer churn prediction model from start to finish).
Machine learning engineers at mid-level build scalable pipelines – for example, setting up an ML system that handles millions of data points in production. You optimize model performance and ensure everything runs smoothly in real-world use.
Skills: Deepening expertise in areas like deep learning, NLP, or computer vision (depending on the job). Also, knowledge of cloud platforms (AWS, Azure) and ML frameworks (TensorFlow, PyTorch) becomes important here.
This role is similar to ML engineer but sometimes leans a bit more on integrating AI into applications. For instance, you might be an AI developer building the AI features in a mobile app (like an image filter using computer vision). You understand both the AI model and the software architecture.
Collaboration: You’ll work closely with product teams and maybe DevOps folks to deploy models as services.
Many mid-level roles specialize in subfields of AI.
As an NLP (Natural Language Processing) Engineer, you focus on language models, chatbots, text analytics, etc. A Computer Vision Engineer works on image and video analysis, like object detection, facial recognition, or medical imaging AI.
These roles often require a mastery of specific techniques (e.g., transformers for NLP, convolutional neural networks for vision).
Exciting aspect: You get to solve domain-specific challenges and often work with unique datasets (text corpora, image databases).
Yes, product managers exist in AI too – and they’re incredibly important. An AI Product Manager is the person who sits at the intersection of business, users, and the AI tech team. In this role, you don’t necessarily code, but you need a solid understanding of AI to make decisions.
You might define the strategy for an AI-driven product, prioritize features (like what an AI should do for customers), and coordinate between engineers, designers, and stakeholders.
Who is this for: Often people who started technical but enjoy the big-picture strategy, or someone from a business side who learned enough tech to talk AI with the engineers. It’s mid-level or higher (some PM experience is usually required).
By now, we all know that consultants make the big (big) bucks!
By mid-level, some professionals become consultants, either within a consulting firm or independent. As an AI consultant, you advise companies on how to use AI effectively.
For the Jack (or Jacqueline) of all trades: It’s a role for someone who likes variety and big-picture problem solving.
These roles support the AI pipeline. A data engineer builds and maintains the data infrastructure that AI models rely on (databases, data pipelines, etc.).
An ML Ops (Machine Learning Operations) Specialist focuses on the deployment, monitoring, and maintenance of AI models in production. Think of ML Ops as similar to DevOps but for AI systems . They ensure that models are versioned, reproducible, and performing well over time.
These roles are great for those who love the mix of software engineering and AI, and ensure that all the AI magic doesn’t fall apart after the model is built!
At the mid-career stage, you might also start taking on lead roles on projects, even if your title isn’t “manager.” For example, you could be the technical lead for an AI feature, coordinating a few colleagues. This is a chance to develop leadership skills and possibly pave the way to management if that’s your interest.
One thing to note: AI is a fast-moving field. Continuous learning remains a big part of the job at mid-level. You might find yourself learning a new programming language or a new AI model architecture that just came out in a research paper.
Embrace it. This constant evolution is what makes an AI career exciting (you’ll never be bored, that’s for sure!).
As you gather significant experience and a track record of successful projects, you may move into senior or management roles in AI. These positions involve strategy, oversight, and often bridging AI with broader business goals. Here are examples of senior AI career paths:
This is a role where you might still be hands-on with the technology, but you’re also coordinating the work of others. As an AI team lead, you guide a group of data scientists or ML engineers, set coding standards, review their work, and mentor junior members.
You’re the go-to person for tough technical challenges. Impact: You influence the direction of projects and ensure quality, making big-picture technical decisions (e.g., which AI tools or models to use for a given problem).
Different from a technical lead, a Project Manager in AI focuses on timelines, deliverables, and cross-team communication. If an organization has multiple AI initiatives, an AI Program Manager might oversee them to ensure they align with company objectives.
This role is less about coding and more about coordination and strategy. It's ideal for someone who understands AI enough to communicate with the tech teams, but thrives in planning and leadership.
Many companies now have a Head of AI or Director of Machine Learning who oversees all AI development. In this role, you’re setting the vision.
For example, you might decide “our company should invest in AI personalization” and then allocate resources to make it happen. You manage multiple teams or projects, report to executives, and often are responsible for hiring and scaling the AI teams.
Prerequisite: Strong experience delivering AI solutions and the ability to translate business goals into AI strategy. Communication skills are key – you’ll be explaining AI to non-technical executives and ensuring your team has what it needs.
A few organizations (especially larger tech companies or those heavily focused on AI) have an executive role like Chief AI Officer or VP of Artificial Intelligence. This is a C-suite or near-C-suite position.
The person in this role ensures the company as a whole is leveraging AI opportunities. They might work on partnerships, guide research investments, and handle high-level decisions like ethical AI guidelines for the company.
Think: a mix of thought leader, strategist, and top-level manager. To reach this level usually requires over a decade in the field and recognized expertise.
This is a senior technical role (often client-facing in companies that provide AI solutions, or internally in big enterprises).
An AI architect designs the overall system that will incorporate AI – how data flows from source to model to user application. They make high-level design choices and ensure that the AI system will be scalable, secure, and integrated with other systems.
Technical background: This type of role is ideal for someone with a strong technical background who enjoys system design and has a broad knowledge of different AI tools.
If you stayed closer to academia or R&D, there are no roles for you in AI. Just kidding!
Senior roles could include Principal Research Scientist or Research Lab Director. These involve leading research agendas, securing funding or budget for research, and guiding junior researchers.
In industry research labs (like Google AI, OpenAI, etc.), principal researchers decide which scientific problems to tackle that align with company interests.
In academia, seasoned AI researchers become tenured professors leading research groups.
Pushers needed!: So, if you love pushing the frontier of AI knowledge, this is a rewarding senior path (though it often requires a PhD and significant publications).
Success in AI careers isn’t only about climbing the ladder to management.
Some senior AI professionals remain as individual contributors (ICs) doing high-level technical work (often called Principal Engineer or Distinguished Scientist roles). These IC roles let you stay deep in the tech without managerial duties, yet still be recognized at a senior level.
The AI field offers multiple ways to lead; whether you're leading innovation, people, or both.
Worried that you don’t have a coding or math background? DOn't even worry about it.
While AI seems highly technical (and parts of it are), AI teams need more than just coders.
There are many roles in AI where non-technical or semi-technical professionals can thrive. In fact, experts say you don’t have to be a software engineer or data scientist to work in AI.
Those without coding expertise (and no interest in learning them) can still find engaging roles in the ever-expanding AI landscape. In fact, with the right knowledge and understanding, it's possible to write your own job description.
The key is to leverage the skills you already have and combine them with some new AI knowledge.
In an era when AI is dubbed the “new electricity” powering the modern world, being part of this field means you’re at the cutting edge of innovation.
It also means you have the chance to shape how AI impacts society – from making businesses more efficient to improving people’s lives with smart products. That’s truly inspiring! So gear up, polish that resume (with those new AI projects and certifications), and take the first step.
