Becoming An Artificial Intelligence Engineer, AIE
- Jake Anderson
- Aug 16, 2023
- 6 min read
Updated: Jul 7
Your roadmap to mastering AI skills, earning top certifications, and launching a future-proof career as an Artificial Intelligence Engineer.
Becoming an Artificial Intelligence Engineer, AIE – learn all about what an Artificial Intelligence Engineer does and how to get into this exciting career field.
This study merges key statistics on America’s high-tech workforce with livability factors in over 150 cities to find the best places to live or relocate to as an IT professional.
What Is AI Engineering?
AI Engineer Responsibilities
AI engineers play an essential role in organizations that use AI. They chart the AI strategy and define the problems to be solved with AI. In addition, they’re in charge of building AI development and production infrastructure and then implementing them. Here are some specific tasks and responsibilities of an AI engineer:
Create and manage the AI development and production infrastructure.
Conduct statistical analysis and interpret the results to guide and optimize the organization’s decision-making process.
Automate AI infrastructures for the data science team
Build AI models from scratch and help product managers and other stakeholders with analysis and implementation
Transform machine learning models into APIs that can be integrated with other applications
Collaborate across teams to help with AI adoption and best practices
Salary And Job Outlook
AI engineering is a specialized field that has promising job growth and tends to pay well.
According to Glassdoor, the median base salary for an AI engineer is $101,991 in the United States. Falling under the categories of Computer and Information Research Scientist, AI engineers have a median salary of $131,490, according to the US Bureau of Labor Statistics.
There is a projected job growth of 22 percent between 2025 and 2030, which is much faster than the average for all occupations (8 percent).
AI engineers typically work for companies like Google, IBM, and Meta, helping them improve their products, software, operations, and delivery. But, more and more, they may also be employed in government and research facilities that work to improve public services.
Hiring growth for artificial intelligence specialists, including engineers, has grown 74 percent annually for the past four years, according to LinkedIn’s 2024 Emerging Jobs report.
How To Become An AI Engineer, AIE
Becoming an AI engineer requires basic computer, information technology (IT), and math skills, which are critical to maneuvering artificial intelligence programs. Here’s how you can get started in AI engineering.
To build a successful career in artificial intelligence, you’ll need to develop a solid foundation in several key technical areas, along with familiarity with the tools and techniques that AI professionals use today. Programming skills are essential, with Python leading the way as the most widely used language in AI and machine learning, supported by powerful libraries like NumPy, pandas, and Scikit-learn. R remains valuable for statistical modeling, while C++ and Java are often used for performance-intensive applications, and Julia is gaining traction in some advanced AI projects.
A strong grasp of mathematics is equally critical. This includes linear algebra concepts such as vectors, matrices, and eigenvalues; calculus, especially partial derivatives used in optimization; and probability and statistics to model uncertainty and deeply understand algorithm behavior. Beyond math, you’ll need practical knowledge of machine learning and deep learning algorithms, ranging from traditional approaches like linear and logistic regression, k-means clustering, and random forests, to advanced architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, generative adversarial networks (GANs), and the increasingly important transformer models used in natural language processing.
Modern AI work heavily relies on frameworks like TensorFlow and PyTorch, which have become the dominant platforms, alongside high-level tools such as Keras for rapidly building neural networks, Hugging Face Transformers for NLP, and ONNX for ensuring model portability across environments. Because AI engineering often involves massive datasets, familiarity with big data and data engineering tools is also important, including Apache Spark and Hadoop for distributed processing, as well as working with SQL and NoSQL databases like MongoDB, Cassandra, or cloud-based data lakes.
An AI engineer today is also expected to understand how to prepare and pipeline data effectively, using orchestration tools like Airflow or Kubeflow to manage workflows. With most AI models ultimately deployed in production environments, experience with cloud platforms such as AWS SageMaker, Google Cloud AI Platform, or Azure ML Studio, along with containerization and orchestration tools like Docker and Kubernetes, has become highly desirable. Finally, as responsible AI becomes a priority, knowledge of fairness, ethics, explainability, and privacy is increasingly valued, supported by tools such as SHAP, LIME, and Fairlearn.
Apply for jobs.
Applying for a job can be intimidating when you have little to no experience in a field. But it might be helpful to know that people get hired daily for jobs without experience. There are many ways to enhance your resume for success in the job search. For AI engineering jobs, you’ll want to highlight specific projects you’ve worked on for jobs or classes that demonstrate your broad understanding of AI engineering.
Once you’ve applied for jobs, you’ll hopefully get an interview. However, don’t be discouraged if you apply for dozens of jobs and don’t hear back – data science, in general, is such an in-demand (and lucrative) career field that companies can receive hundreds of applications for one job.
Preparing for the interview requires practice and preparation, especially for tech jobs like an AI engineer. You’ll want to improve your interview skills to prove to hiring managers that you’re perfect for the job. In addition, you’ll be expected to explain your reasoning for developing, deploying, and scaling specific algorithms. These interviews can get very technical, so be sure you can demonstrate how you solved a problem and why you chose to solve it that way.
Get work experience.
There may be several rounds of interviews, even for an entry-level position or internship. But if you land a job, it’s time to prove yourself and learn as much as possible. Then, you’ll be able to apply the skills you learned toward delivering business insights and solutions that can change people’s lives, whether in health care, entertainment, transportation, or consumer product manufacturing.
Explore Artificial Intelligence With Chauster.
Launch your career as an AI Engineer with Chauster’s industry-aligned AI Engineer professional courses. Our comprehensive program is designed to equip you with the expertise to transform big data into actionable business insights using advanced machine learning techniques. You’ll dive deep into real-world applications, mastering how to build and deploy algorithms with Apache Spark and develop powerful models and neural networks with Keras, PyTorch, and TensorFlow. With Chauster, you don’t just learn theory — you gain practical, hands-on experience that mirrors what employers look for in today’s competitive AI landscape.
Don’t wait to future-proof your career. Enroll with Chauster UpSkilling Solutions today and start building the in-demand skills that will set you apart as an AI Engineer. Take the next step toward unlocking exciting opportunities in one of the world’s fastest-growing tech fields. Your journey into artificial intelligence starts here.
About Steve Chau
Steve Chau is a seasoned entrepreneur and marketing expert with over 35 years of experience across the mortgage, IT, and hospitality industries. He has worked with major firms like AIG, HSBC, and (ISC)² and currently leads TechEd360 Inc., a premier IT certification training provider, and TaoTastic Inc., an enterprise solutions firm. A Virginia Tech graduate, Steve’s career spans from founding a teahouse to excelling in banking and pivoting into cybersecurity education. Known for his ability to engage underserved markets, he shares insights on technology, culture, and professional growth through his writing and leadership at Chauster Inc.
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