You don’t need a PhD or experience in the tech industry to engage with Artificial Intelligence anymore. AI jobs at the entry-level open opportunities for you as a new graduate, self-taught programmer, or even someone shifting careers. Now that the healthcare, finance, and even retail industries are adopting AI technologies, the opportunities are endless.
What exactly do these positions entail? What pre-requisite skills are necessary? And how does one secure a job without prior years of workforce experience? From the best beginner-level AI positions to the certifications that will add value to your resume, this guide will outline everything you need to know to launch your career in artificial intelligence.
Let’s start with your first AI job and head into the future.
What Are AI Jobs?
AI jobs refer to roles that involve developing, applying, or managing artificial intelligence systems to solve problems, improve efficiency, and automate tasks across various industries. These jobs span both technical and non-technical responsibilities, depending on the nature of the work.
Technical AI Roles: These require strong skills in programming, data analysis, and machine learning, and often involve building or optimizing AI models.
- 1. Machine Learning Engineer: Designs and implements machine learning algorithms that can learn from and make predictions on data.
- 2. Data Scientist: Analyzes complex data to find patterns and insights, often using AI techniques like supervised learning or clustering.
- 3. AI Research Scientist: Conducts cutting-edge research to create new AI models, often focusing on deep learning, NLP, or reinforcement learning.
- 4. Computer Vision Engineer: Builds systems that interpret visual data, such as image recognition, object detection, or autonomous vehicles.
- 5. Natural Language Processing (NLP) Engineer: Focuses on making machines understand human language for chatbots, voice assistants, and sentiment analysis tools.
- 6. Data Engineer: Builds data pipelines and infrastructures that support AI and machine learning workflows.
- 7. AI Software Developer: Creates applications that incorporate AI features into traditional software, like recommendation systems or fraud detection tools.
Non-Technical AI Roles: These roles are critical for the adoption, strategy, communication, and ethical oversight of AI systems but don’t necessarily require coding or algorithm development skills.
- 1. AI Product Manager: Manages AI-based products from idea to deployment, aligning business goals with technical capabilities.
- 2. AI Project Manager: Coordinates AI development timelines, resources, and stakeholder communications to ensure successful implementation.
- 3. Data/AI Analyst: Interprets the outputs from AI systems, helping businesses make data-driven decisions using AI-generated insights.
- 4. AI Ethicist / Policy Analyst: Works on the ethical and societal impacts of AI, developing guidelines for responsible AI usage.
- 5. Technical Writer (AI Focused): Creates documentation, tutorials, and manuals to explain complex AI systems to non-technical users or internal teams.
- 6. Sales & Marketing for AI Solutions: Helps businesses understand the value of AI products, handles client queries, and ensures alignment between customer needs and technical capabilities.
- 7. AI Trainer / Data Annotator: Labels and prepares data to train AI models (e.g., tagging images or categorizing text for NLP).
How to Start a Career in AI Without Experience
You can break into AI without a formal background by:
- Learning Online – Take beginner-friendly courses on platforms like Coursera or Udemy.
- Practicing Projects – Build small AI projects (e.g., image recognition, sentiment analysis) and share them on GitHub.
- Learning Python & Tools – Focus on Python, Pandas, Scikit-learn, TensorFlow, and Jupyter Notebooks.
- Contributing to Open Source – Help with AI repositories on GitHub to gain real-world experience.
- Joining Communities – Network on Reddit, LinkedIn, or Kaggle to learn and find opportunities.
- Building a Portfolio – Showcase your work in a neat GitHub or personal website.
- Applying for Jobs – Start with internships, junior AI roles, or data annotator jobs.
No degree? No problem—skills, projects, and consistency matter most!
10 Entry-Level AI Jobs to Kickstart Your Career
1. Junior Machine Learning Engineer
Starting your AI career as a Junior Machine Learning Engineer means working directly with models that power smart systems. You’ll be responsible for assisting in designing, training, and optimizing machine learning models under the guidance of experienced engineers.
- What You Do: Write Python scripts, test models, clean data, and support deployment.
- Required Education: Bachelor’s degree in Computer Science, Data Science, AI, or similar.
- Skills Needed: Python, Scikit-learn, TensorFlow/PyTorch, NumPy, Git.
- Expected Salary: $70,000 – $95,000/year.
- Career Scope: Progress to ML Engineer, AI Specialist, or Deep Learning Expert.
2. AI Research Assistant
AI Research Assistants help scientists and researchers by handling the groundwork: conducting experiments, analyzing data, and reviewing academic literature. This is a great starting point for anyone interested in theoretical AI or academic paths.
- What You Do: Assist with data analysis, algorithm testing, and technical writing.
- Required Education: Bachelor’s or Master’s in CS, AI, Math, or a related field.
- Skills Needed: Python, Jupyter Notebooks, and academic research skills.
- Expected Salary: $50,000 – $75,000/year.
- Career Scope: Ideal route toward an AI research scientist or a PhD in AI.
3. Entry-Level Data Scientist
Data Scientists interpret large volumes of data to provide actionable insights. As an entry-level professional, you’ll help build predictive models and assist in shaping business decisions using AI-driven data analysis.
- What You Do: Analyze data, create ML models, and present insights.
- Required Education: Bachelor’s in Data Science, Mathematics, or Statistics.
- Skills Needed: Python, R, SQL, Pandas, Seaborn, Scikit-learn.
- Expected Salary: $65,000 – $90,000/year.
Career Scope: Evolve into a Senior Data Scientist or Data Science Manager.
4. Data Analyst (AI-Focused)
AI-focused Data Analysts are a bridge between raw data and machine learning models. They help AI teams with initial data exploration and ensure the data is clean, reliable, and usable.
- What You Do: Clean data, build dashboards, and assist in feature engineering.
- Required Education: Bachelor’s in Business Analytics, Statistics, or CS.
- Skills Needed: Excel, SQL, Tableau/Power BI, basic ML knowledge.
- Expected Salary: $55,000 – $80,000/year.
- Career Scope: Can lead to Junior Data Scientist or AI Analyst roles.
5. NLP Assistant
NLP Assistants support the development of AI that understands and interprets human language. This role involves preprocessing text, training basic NLP models, and evaluating performance.
- What You Do: Work with text data for chatbots, sentiment analysis, etc.
- Required Education: Bachelor’s in Linguistics, CS, or AI.
- Skills Needed: Python, spaCy, NLTK, regex, and tokenization.
- Expected Salary: $60,000 – $85,000/year.
- Career Scope: Can grow into an NLP Engineer or AI Language Specialist.
6. Computer Vision Intern
A Computer Vision Intern helps create models that interpret visual information. You’ll work with teams to label data, preprocess images, and sometimes run basic image classification tasks.
- What You Do: Assist in training vision models and labeling image/video data.
- Required Education: Currently studying or have completed a CS or Engineering degree.
- Skills Needed: OpenCV, PIL, TensorFlow/Keras, image processing.
- Expected Salary: $20,000 – $40,000/year (internship stipend).
- Career Scope: Grow into a Computer Vision Engineer or an Image Processing Analyst.
7. AI Product Analyst
AI Product Analysts evaluate how well AI features serve users and what can be improved. This is a great role for someone interested in combining data, business, and tech.
- What You Do: Analyze product performance, suggest data-backed improvements.
- Required Education: Bachelor’s in CS, Business, or Analytics.
- Skills Needed: A/B testing, SQL, data interpretation, product thinking.
- Expected Salary: $60,000 – $85,000/year.
- Career Scope: Evolve into an AI Product Manager or Analytics Lead.
8. AI/ML Intern
This internship role allows students and fresh graduates to experience real-world AI development. You’ll assist teams by labeling data, writing scripts, or testing models.
- What You Do: Data cleaning, documentation, and model testing support.
- Required Education: Enrolled in or completed a CS, Math, or Engineering degree.
- Skills Needed: Python, Jupyter Notebooks, Git, willingness to learn.
- Expected Salary: $15 – $25/hour or $20,000 – $40,000/year.
- Career Scope: May lead to Junior ML Engineer or AI Analyst roles.
9. AI Support Engineer
AI Support Engineers bridge the gap between technical tools and their users. They troubleshoot AI software, guide customers, and report bugs to the development team.
- What You Do: Resolve technical issues and provide AI product support.
- Required Education: Bachelor’s in IT, CS, or Software Engineering.
- Skills Needed: Python basics, API understanding, and communication skills.
- Expected Salary: $50,000 – $75,000/year.
- Career Scope: Growth into Solutions Engineer or Technical Consultant roles.
10. AI Data Annotator / Labeling Specialist
Labeling Specialists play a critical behind-the-scenes role by preparing the training data for AI systems. This is often the starting point for non-coders who want to enter AI.
- What You Do: Label data (images, text, audio) for supervised learning models.
- Required Education: High School diploma or Associate’s degree; training provided.
- Skills Needed: Accuracy, familiarity with a tool (e.g., Labelbox), and understanding of AI data.
- Expected Salary: $30,000 – $50,000/year.
- Career Scope: With experience, grow into a QA Analyst, AI Trainer, or Dataset Manager.
Best Courses for Entry-Level AI Jobs
Starting a career in AI becomes easier when you choose the right learning resources. Many top online platforms offer beginner-friendly courses that cover core AI concepts like machine learning, Python programming, and neural networks. These courses help build a strong foundation and are often recognized by employers.
Top Platforms & Recommended Courses:
Coursera
- Machine Learning by Andrew Ng (Stanford University)
A must-take foundational course covering supervised learning, regression, and neural networks. - AI For Everyone – Non-technical intro to AI applications.
edX
- CS50’s Introduction to Artificial Intelligence with Python (Harvard)
Great for beginners with some programming knowledge. - AI MicroMasters by Columbia University
Udacity
- Intro to Machine Learning with PyTorch or TensorFlow
Hands-on projects and real-world examples. Ideal for beginners aiming for AI roles. - AI Programming with Python Nanodegree
Teaches Python, NumPy, Pandas, Matplotlib, and basic neural networks.
Fast.ai
- Practical Deep Learning for Coders
A free and fast-paced course focused on hands-on deep learning using PyTorch. Ideal if you prefer learning by doing.
Google AI
- Machine Learning Crash Course
Interactive exercises and real-world case studies for beginners in ML.
Top Certifications to Land Your First AI Role
Getting certified is a great way to validate your AI skills, especially when starting. Recognized certifications from tech giants like Google, IBM, and Microsoft can significantly enhance your resume and increase your chances of landing an entry-level AI job.
1. Google TensorFlow Developer Certificate
- Focus: Deep learning and TensorFlow-based models
- Best For: Those familiar with Python and basic ML concepts
- Why It’s Valuable: Recognized by AI employers worldwide
- Cost: ~$100
2. IBM AI Engineering Professional Certificate (Coursera)
- Focus: Machine learning, deep learning, Python, and neural networks
- Best For: Beginners to intermediate learners
- Why It’s Valuable: Hands-on projects + IBM badge recognition
- Duration: 6 months (part-time)
3. Microsoft Certified: Azure AI Fundamentals
- Focus: Core AI concepts, responsible AI, and Azure AI services
- Best For: Beginners, especially those interested in cloud-based AI
- Why It’s Valuable: Good for roles involving Microsoft’s ecosystem
- Cost: ~$99
4. Stanford’s Machine Learning Certificate (via Coursera)
- Instructor: Andrew Ng
- Focus: ML fundamentals like regression, SVM, neural networks
- Why It’s Valuable: Trusted course that sets a solid foundation
5. AWS Certified Machine Learning – Specialty
- Focus: Building, training, and deploying ML models on AWS
- Best For: Those aiming for cloud + AI integration roles
- Why It’s Valuable: In-demand for roles involving AI in cloud environments
Online Platforms to Learn AI Basics for Free
If you’re just starting and want to explore artificial intelligence without spending money, several trusted online platforms offer free, high-quality AI courses and tutorials. These resources cover foundational topics such as machine learning, Python programming, and data science essentials — perfect for building your AI knowledge base.
Top Free Platforms to Learn AI Basics:
FreeCodeCamp
- Offers comprehensive Python and machine learning tutorials.
- Includes hands-on coding exercises and project-based learning.
- Ideal for beginners who prefer learning by doing.
Khan Academy
- Provides foundational math courses crucial for AI: linear algebra, calculus, and statistics.
- Easy-to-understand video lessons that build core skills required for AI.
- Great for learners who want to strengthen their math fundamentals.
MIT OpenCourseWare
- Access to full university-level courses on AI, machine learning, and data science.
- Includes lecture videos, notes, and assignments — all free.
- Best for self-motivated learners looking for in-depth material.
Google’s Machine Learning Crash Course
- Interactive lessons with practical exercises using TensorFlow.
- Beginner-friendly introduction to key ML concepts and algorithms.
- Includes real-world examples and visualizations.
YouTube Channels
- Channels like 3Blue1Brown, Sentdex, and Two Minute Papers offer excellent AI tutorials and explanations.
- Free visual content for conceptual understanding.
How to Write a Resume for Entry-Level AI Jobs
You should feature anything you have taken in school, worked on, and any similar experiences in your resume for AI entry-level jobs. For the first part, express your enthusiasm for AI and describe your most important skills. Showing your knowledge in AI can be accomplished with courses in machine learning, data structures, and statistics. It’s important to mention the projects you did—describe the issues you faced, what solutions you used, and the positive outcomes you achieved, such as improved accuracy or quick results.
If you didn’t get to work with AI, describe the programming, analyzing of data and teamwork abilities you learned. To show how you’ve performed, tell recruiters what tasks you carried out, like “developing,” “implementing,” “managing,” or “analyzing.” When you can, include concrete figures so they understand better how strong you were. Be sure to add Python, TensorFlow, and Scikit-learn to your resume and try to keep it short and easy to understand. Ultimately, insert keywords from the job description into your resume to help you stand out.
Entry-Level AI Jobs in Healthcare
If you want to use artificial intelligence in healthcare, then entry-level jobs are a good way to get started. Frequently, these roles are involved in helping build healthcare solutions using data, including examining patient files, taking part in building diagnostics methods or assisting in building systems that understand medical writing. You do not need to have strong medical knowledge to start, but knowing healthcare data and technology well could make you stand out. Those who choose these positions can improve patient health care, carry out research, and make the healthcare system more efficient.
Common Responsibilities:
- Analyze patient and hospital data using Python or R
- Clean and prepare healthcare datasets for AI models
- Support the development of diagnostic or predictive algorithms
- Work with NLP models to process clinical notes or medical records
- Assist in building dashboards or tools for healthcare analytics
- Collaborate with medical professionals and data scientists
Remote Entry-Level AI Jobs You Can Do from Anywhere
With the rise of remote work, many companies now offer entry-level AI roles that you can do from anywhere in the world. These remote positions are perfect for beginners looking to gain experience without relocating. They typically involve tasks like data labeling, where you help train machine learning models by annotating images, text, or audio. Other common responsibilities include evaluating AI models for accuracy, writing simple Python scripts, or cleaning and organizing datasets. These jobs often serve as stepping stones into more advanced AI roles, giving you hands-on experience with real-world data and AI workflows while building your portfolio. All you need is a reliable internet connection, basic programming skills, and a strong willingness to learn.
Common Remote AI Tasks:
- Data annotation (images, audio, text, video)
- Evaluating the performance of machine learning models
- Writing basic Python scripts to preprocess or analyze data
- Labeling and categorizing content for NLP or computer vision projects
- Organizing and cleaning large datasets
- Assisting AI research teams with documentation and reporting
Entry-Level AI Job Opportunities for International Students
Set up your career in artificial intelligence by tapping into a range of introductory jobs through internships and working as a freelancer. Many schools in the U.S. and elsewhere offer OPT or CPT, which allow students to combine learning with working in their chosen fields early in their professional lives. With such programs, students may be able to obtain internships, work as research assistants, or enter junior AI jobs.
International students have the option to earn money by working online at Upwork, Toptal or Freelancer and offering services including labeling data, scripting in Python or developing models using AI. Being involved in online contests and joining open-source teams increases their chances of being noticed by organizations worldwide. You should focus on your LinkedIn profile, join AI communities, and follow companies that allow a visa when launching your AI career outside the United States.
Ways International Students Can Kickstart an AI Career:
- Apply for CPT/OPT Internships through your university to gain experience in U.S. companies.
- Participate in AI research projects as a student or assistant.
- Join freelancing platforms like Upwork, Fiverr, and Toptal to offer entry-level AI services remotely.
- Compete in Kaggle competitions to gain practical experience and showcase your skills.
- Contribute to open-source AI repositories on GitHub to build credibility.
- Attend virtual AI meetups or hackathons to network and find global job leads.
- Target companies that sponsor international hires, especially in tech-forward countries like the U.S., Canada, Germany, and the Netherlands.
FAQs
1. Is having a degree necessary for a first position in AI?
Not always. Having a computer science degree is useful, but many companies value online course graduates, bootcamp trainees, and those with impressive projects.
2. What languages are useful for me to learn in programming?
Python is the language the field of AI relies on most. If you know more tools, like R, SQL, and libraries including TensorFlow, PyTorch, and Scikit-learn, it is a valuable addition.
3. Is it possible to find an AI job without any experience?
Yes. One way is to gain skills by undertaking personal work, using internships, taking part in open-source and joining Kaggle platforms.
4. Which fields often hire people just beginning their careers in AI?
Tech, healthcare, finance, e-commerce, education, and even more sectors have important AI roles. Companies in all sectors are increasingly seeking AI assistance in most areas.
5. How much money could I expect as a beginner in the field of AI?
People starting in this field usually earn between $60,000 and $90,000 per year in the U.S., again depending on the company and location.
6. Do remote jobs in AI exist for those who have just started?
Yes. A lot of businesses hire AI interns and junior engineers, who help with activities like data labeling, checking models, and using Python code.
Conclusion
Entry-level AI jobs offer an exciting gateway into one of the fastest-growing fields in tech. With the right mix of foundational skills, hands-on projects, and continuous learning, anyone can start building a career in AI—no advanced degree required. Start small, stay curious, and your first AI role could be closer than you think.
