Generative artificial intelligence (AI) is having a big impact on the way companies conduct business today—making the technology front of mind for hiring managers across a range of industries.
Machine learning and generative AI modeling were two of the fastest-growing skills on Upwork over the past year. In fact, the AI & machine learning skills category saw a 70% year-over-year growth in gross services volume (GSV). What’s clear is that clients are coming to Upwork to find freelancers with AI skills.
By creating a great AI skills resume and cover letter, you can find—and get hired for—new AI career opportunities. These tips will help you get started.
AI’s role in today’s job market
You can find people working with AI in a wide range of roles, including:
- Office manager
- Prompt engineer
- Midjourney artist
- Administrative assistant
- Chatbot developer
- Scientific researcher
- Financial analyst
- Data scientist
- AI writer
- Robotics engineer
- Data analyst
- Computer vision engineers
Many roles and industries now rely on AI to help streamline computer-based work. AI technologies help workers analyze data, draft communications, create mockups, and more.
But you’ll also find the need for people with AI skills in industries that typically rely on manual labor, too. A forest ranger may use AI to assess data collected by sensors on animal trackers or park cameras, while a factory operations manager may use AI to help ensure that production lines are operating at peak efficiency.
These are jobs that AI won’t replace, but will rather improve the efficiencies and outcomes through collaboration.
10 top artificial intelligence skills for resumes
You should mention any relevant qualifications, technical abilities, and AI techniques you have experience with when preparing your resume for an AI job search. Some AI terms and skills sets you may want to highlight on your resume or LinkedIn profile include:
- Scripting and automation
- Prompt engineering
- Programming languages for AI
- Machine learning
- Neural networks and deep learning
- Natural language processing (NLP)
- Cognitive computing skills
- Data analytics
Spelling out these skills on your resume can help your job application catch the attention both human recruiters and any AI-powered applicant tracking systems they may be using, too!
1. Prompt engineering
Prompt engineering is the process of instructing large language models (LLMs) via text commands.
Sometimes prompts are very simple. For example, when I wanted ChatGPT to analyze a spreadsheet, I simply uploaded the file and stated the information I wanted.
Other prompts need to be more complex, such as this one that I used to ask ChatGPT for help developing a video outline:
The way you write a prompt can change the results generated by AI. And, because many AI tools—including ChatGPT—retain conversation history, you can craft prompts that layer on top of each other.
Skilled prompt engineers can coax high-quality, complex results out of AI tools by using the right requests.
2. Programming languages for AI
Programming skills are imperative for AI development. Skilled programmers build and train the machine learning models that power tools like ChatGPT—and they typically know one or more computer languages.The most in-demand programming languages for AI programmers are:
- Python: A general-purpose language often used in the development of generative AI applications
- Java: A popular language used in machine learning algorithms and neural networks
- R: A language used for data visualization and developing neural networks
- Lisp: A 64-year-old programming language used to manipulate strings of data
If you know one of these languages and are interested in working with AI, you’ll want to be very specific about your skills and experience level. That said, AI tools like ChatGPT can help you continue to level up and even debug your code—just be careful not to share proprietary information with public tools!
3. Scripting and automation
Automation scripts that leverage machine learning to do routine tasks quickly are one common use for the Python language. For example, if a business owner needs to pull a set of CSV reports every Friday, sort the data by source, and compare it to the previous week, a script can speed up this process.
Similarly, if a marketing team regularly scrapes the web for keyword data, they could benefit from a Python script that automates this data-collection process.
More companies are becoming familiar with the benefits of scripting and automation, and demand is increasing for freelancers with this skill set.
4. Machine learning
Machine learning is a subset of artificial intelligence that automates data analysis and iterative learning. In part, it uses data to create accurate predictions.
You can find machine learning engineers working on e-commerce shopping recommendation algorithms, facial recognition programs, digital ad platforms, and more.
This prediction-based approach to AI is a central part of generative AI. As tools like ChatGPT become more and more commonplace, more companies will need skilled machine learning pros on hand.
5. Neural networks and deep learning
Neural networks are a complex approach to machine learning. In a neural network, multiple layers of processing connect to each other in nodes. These connections are structured so that information passes through them in different ways, resulting in a variety of outputs.
If a neural network has more than three layers, it’s considered a deep learning algorithm. Being specific about any skills in or experience with neural networks and deep learning can help you find interesting companies to partner with and projects to work on
6. Natural language processing (NLP)
NLP is a subset of computer science that’s focused on programming computers to understand language. Computer systems trained with NLP may be able to interpret written text as well as spoken audio—and produce responses that mimic our speech patterns, too.
When you use a conversational AI tool, you’re experiencing NLP in action. As more businesses roll out customer-facing AI applications, they’ll need NLP engineers to help make sure that the user experience is as natural and seamless as possible.
7. Cognitive computing skills
While NLP simulates human speech, cognitive computing attempts to generate outputs that simulate human responses. Cognitive computing engineers work to create computer systems that can recognize images, patterns, sentiment, risk, and more, and respond to these stimuli in a way that tracks with how a person would respond.
However, computers don’t think or have cognitive abilities similar to a person’s. Instead, machines use statistical algorithms to learn patterns and predict outcomes. These outcomes can include human-like responses and behaviors.
That being said, the field of cognitive computing has many uses in a wide variety of industries, and leverages many other AI skills, including NLP, vision recognition, and human-computer interaction. This field is especially promising for highly regulated industries like banking, where a nuanced AI tool might be essential.
8. Data analytics
AI skills are very useful when it comes to data analytics, as machine learning can greatly speed up the processing and understanding of large data sets. You can combine AI and data analytics experience to more quickly find, clean, and build reports out of data for a variety of business applications.
Demonstrating AI proficiency on your resume
Because hiring managers are often looking for very specific AI skills, you’ll want to be as clear as possible when writing a resume or proposal.
Simply saying you’re a programmer who knows “multiple languages” may not be enough to make your resume stand out. Indicating that you’re a pro with five years of experience using Python to build neural networks, though, can quickly land you on a company’s shortlist of great candidates.
Quantify your achievements
Assigning specific, quantifiable outcomes to your work in AI is also important.
A quantifiable outcome can be expressed as a countable number. For example:
- Quantifiable skill statement: My team used AI-driven sentiment analysis to improve post-purchase customer satisfaction by 20%, quarter over quarter.
- Non-quantifiable skill statement: I had my team use an AI text analysis tool to find customer pain points when shopping in our retail stores.
- Quantifiable skill statement: By building a custom application that used machine learning to spot patterns in production line data, we reduced unexpected line shutdowns by 10%.
- Non-quantifiable skill statement: We used ChatGPT to analyze data and improve efficiency at our London-area production facility.
You may not be able to quantify the outcome of every project you’ve ever worked on, and that’s okay. But try to do this whenever you can.
Highlight collaborative projects
AI is a very collaborative and cross-functional field. If a company wants to roll out a new chatbot to support its customer service team, you may find all of the following people working on the project:
- Customer service representatives to provide input on needs
- Copywriters to enhance the chatbot’s style and tone
- User experience designers to build an appealing interface
- AI engineers or developers to create the chatbot’s algorithms
- Cybersecurity advisors to ensure that sensitive information is protected
- Social media managers to promote the new chat experience to customers
If you’ve worked on AI projects with people from different teams, mention this! It shows that you’re ready to jump in and work collaboratively with others at a new company.
Stay up-to-date with relevant certifications and training
The field of AI changes rapidly. To grow your career in the industry at a comparable pace, you’ll need to focus on ongoing AI education and upskilling.
While you can in theory grow your AI knowledge and career without taking any structured courses, getting an AI certification can help you learn new skills quickly and showcase your commitment to the field.
You can find free and low-cost course options on platforms like Coursera and Udemy, or pursue a more formal education path with professional certificates from Stanford University, among others.
How to add AI skills to a resume
You can adapt your existing resume template to highlight your AI achievements and skills by simply working the relevant terms and explanations into your job duties and resume summary. If you’re applying for a job for the first time, then try this resume outline as a starting point.
1. Name and contact information
If you’re applying for a job posting on a company’s website or hiring portal, you’ll want to include your contact information so they can get in touch with you about any potential interviews.
2. Resume summary
Your resume summary statement is a short paragraph that highlights who you are, what your top skills are, and the value you bring to a project team or organization.
3. Achievements and education section
This is where you can highlight any AI certifications, coursework, and education experiences that are relevant for a specific job.
4. Work experience section
You can choose to put your work experience section above or below your education details. This is the spot where you’ll want to outline your years of employment, taking care to note any quantifiable outcomes of your work wherever possible.
5. Technical AI skills and expertise section
The technical AI skills section of your resume expands upon what you covered under education and work experience. Here, you can create a bullet-point overview of your technical expertise or digital skills. Depending on your domain expertise, this might include things like predictive analytics, use of specific AI frameworks or tools like TensorFlow, working with neural networks, ChatGPT prompt engineering, and more.
6. Soft skills section
A successful career in AI isn’t all about technical skills. You’ll also want to work on, and highlight, a variety of soft skills—particularly those related to thinking through problems, facing change head-on, and communicating with others.
Soft skill competencies you may want to highlight on your resume include:
- Adaptability
- Communication
- Creativity
- Critical thinking and problem-solving
- Emotional intelligence
- Analytical thinking
You don’t need to list every relevant skill out in a row—instead, try working them into your descriptions of jobs and projects like this:
“In this role, I led a cross-functional team of five to evaluate problems in current workflows and develop generative AI solutions. When deployed, our solutions led to a 10% increase in delivery speed.”
(If you’re feeling stuck, you can find professional resume writers and resume builders on Upwork!)
Leveraging AI skills in interviews
If your resume catches the attention of a hiring manager, you may be asked to interview—so you’ll want to be prepared to talk about your skills verbally!
Practice discussing your work in the following ways:
- Explain your AI experience in quantifiable terms, just like on your resume
- Walk the interviewer through how your AI skills have evolved over time and show that you’re committed to growth
- Reference specific machine-learning skills and concepts when discussing your approach to real or hypothetical problems
- Explain how you used specific soft and hard skills to address a problem on one of your recent projects
- Be familiar with the history and background of your AI skills—such as how Python use has evolved over time
- Mention any other AI skills that, even if they’re not your specialty, you’ve been exposed to through your work
You can try practicing these statements by yourself, or with a friend. You can even ask ChatGPT to act as an interviewer and create questions for you to answer in real-time.
Talk about other related experiences
While some people have spent their entire career in AI, you aren’t alone if you’ve only moved into this field recently. Don’t hesitate to still talk about your previous professional experiences and how they help you in your current AI work.
- Software engineers can discuss how their approach to software development has changed with the help of AI
- Data scientists might reference how the skills they obtained in their data science work without AI influence the way they interact with the technology now
- Writers could explain how much time AI is saving them when it comes to editing and optimizing content for websites
- Graphic designers may want to discuss how they use AI to create more mockups, versions, and ideas faster than ever before
AI skills equal freelancing opportunities
No matter where you currently are in your AI career, continuing to evolve, define, and explain your skills can help you land more opportunities—including independent work.
49% of hiring managers expect to hire more independent talent as a result of increased generative AI use. They need people on their teams who can use generative AI and think of new ways to leverage these tools’ capabilities.
By creating an Upwork profile that’s optimized to showcase your AI skills, you can connect with a variety of companies working on interesting and exciting AI projects.
