If you’re interested in becoming an AI engineer, you should know that it is now possible thanks to the advances in artificial intelligence. In 1959, Alan Turing predicted that machines would one day be able to compete with humans intellectually. Now, in 2023, this has become a reality.
To make friends with machines & become an AI engineer, there are certain skills and knowledge you need to possess.
To pursue a career in AI engineering, you must consider if it is the right fit for you. AI has seen an impressive 154% growth in the global market, catalyzing an AI revolution. This expansion can be attributed to three major factors:
- The advancement of machine learning.
- The use of cloud computing resources is widespread.
- Developing the way data is collected, stored, and processed.
What is the definition of Artificial Intelligence?
AI makes tasks easier & faster by using technologies such as natural language processing & deep learning. These technologies allow machines to analyze vast amounts of data, recognize patterns, & figure out how to complete tasks. AI used for many of purposes, including:
- Business operations
- Speech recognition
- Self-driving cars
- Chess computers
- Image analysis
By using Artificial Intelligence, we can quickly analyze data & come up with ideas that may not have been considered before.
Why is Artificial Intelligence Important Now?
The development of artificial intelligence has created a debate over its potential impact on the job market. Some worry that AI will cause both technical and non-technical jobs to become obsolete, while others believe it should be used as a tool to make life easier rather than replace humans entirely.
As demand for qualified AI professionals increases, the Organization for Economic Co-operation and Development (OECD) reports more computer jobs have been created. With this in mind, now is an ideal time to pursue a career in AI technology.
What is Artificial Intelligence Engineering?
Artificial intelligence engineering combines systems engineering, software engineering, computer science, & human-centered design to create AI systems that meet people’s goals & objectives.
Why Should You Pursue a Career as an AI Engineer?
There is no need to worry about whether there will be enough jobs for the 4.5 million engineers who graduated in the US in 2020.
The United States has been the leading country in private investment in AI & the growth of new AI companies since 2021. According to a 2020 LinkedIn report on emerging jobs in the US, engineering roles have seen tremendous growth. 50% of popular job roles are related to engineering, & the demand for AI specialists has increased by 74% over the past four years. On average, an AI Engineer in the US earns an annual salary of $167,541 which highlights the great potential for those working in this field.
What is the job of an AI Engineer?
It is important to understand the role & responsibilities of an AI engineer before beginning the process of becoming one. Knowing this information can help you decide if this is the right career path for you. The main duties of an AI engineer include:
- Creating AI models using machine learning algorithms to gain valuable business insights.
- Using statistical analysis & interpretation techniques to make organizational processes more efficient.
- Creating AI models to solve complex tasks by developing, programming, & training networks.
- Creating & maintaining infrastructure for AI.
What are the Steps to Becoming an AI Engineer?
To become an AI engineer, there are certain skills and educational prerequisites that must be met. These include:
Educational Requirements
A Bachelor’s degree in
- Information Technology
- Computer Science
- Statistics
- Data Science or Finance is recommended.
A Master’s degree in
- Computer Science
- Mathematics
- Cognitive Science
- Data Science or a related field
Skills Requirements
Programming Languages
To become a successful AI engineer, you need to be proficient in a few programming languages that are suitable for AI development. Examples include Python, C++, Java, & R. Additionally, you should familiarize yourself with technologies related to AI such as machine learning algorithms & neural networks.
- Python
- Java
- R
- PROLOG
- C++
- Lisp
Python is the language of choice among developers and AI engineers due to its ease of use, concise syntax, broad capabilities, and a vast library. To get started with AI, it’s best, to begin with, a language that suits your learning level.
We suggest starting with either Python or R, as these languages are more suited for portability and multitasking. If you would like to expand your knowledge further, you can then look into learning Java or C++.
Statistics, Calculus, and Algebra
Statistics is an essential part of any algorithm used in artificial intelligence. To create an AI program, you need a good understanding of statistics, calculus, and/or algebra. In the process of building an AI program, you will come across machine learning models like NaĂŻve Bayes or Hidden Markov which require knowledge of probability.
Algorithms and Applied Mathematics are related.
To create or use existing models, it is necessary to have a good understanding of algorithms and mathematics. This knowledge can be applied in many different areas:
- Partial Differential Equations (PDEs)
- Gradient Descent
- Quadratic Programming
- Lagrange is an interpolation technique.
- Optimizing in a convex way
Processing of Natural Language
Natural language processing (NLP) is an interdisciplinary field of computer science, information engineering, linguistics, & AI. As an AI engineer, you will need to use various NLP libraries & tools to process & analyze large datasets in the form of language, audio, & video.
- TextBlob
- CoreNLP
- NTLK
- Sentiment Analytics
- PyNLPI
- Gensim
Networks of Neurons
A neural network is an artificial intelligence system that functions similarly to the human brain. It is used for tasks like pattern recognition, facial recognition, & handwriting recognition. AI engineers use these networks to solve complex problems in their respective fields.
Communication and Working Together Skills
As an AI engineer, you will need to effectively communicate your data findings in order to solve problems for your stakeholders. Good communication skills are essential to build strong relationships with stakeholders.
The ability to analyze information.
To become an AI engineer, you must have the ability to critically analyze data & numbers. Additionally, it is important to ask questions of the data analytics team & collaborate with key stakeholders to evaluate the potential of the data.
Understanding of Business Practices
AI projects require both the development of the user interface & problem-solving. To be successful, you must understand the industry, customers, & company operations in addition to having technical skills. Knowing how to create practical business models with your ideas is essential for success.
Rapid Prototyping of Ideas Iteration
Idea iteration, or rapid prototyping, is a process of rapidly testing ideas to identify the best one. Testing can include models, A/B testing, & natural language processing libraries. Rapid prototyping also involves creating 3D CAD designs of parts or assemblies to help visualize how they will look in reality.
What jobs can AI engineers do?
AI engineers can help organizations make important decisions & anticipate future issues. AI technology is widely used across many industries for a variety of purposes, such as:
- Financial services companies analyze user habits in order to detect & prevent fraudulent activity.
- Manufacturers reassess their supply chains and plan regular maintenance to make sure products are created safely, effectively, and cost-effectively.
- Healthcare organizations use automation to make patient interactions more efficient and to save money on pharmaceutical production.
- Businesses use customer behavior, sentiment, and buying patterns to better understand and engage with their customers.
What types of courses and certifications are available for AI engineers?
- Stanford University School of Engineering offers a Graduate Program in Artificial Intelligence.
- Andrew NG’s course on Coursera.
- An Introduction to TensorFlow for AI, ML, and DL (Coursera)
- Learn How to Build an AI with Artificial Intelligence A-Z on Udemy.
- Take an Udemy course on Reinforcement Learning in Python with Artificial Intelligence.
- Learn the Basics of AI and Machine Learning with LinkedIn Learning.
What duties do AI engineers have?
AI engineers develop AI models using machine learning algorithms and deep learning neural networks to gain business insights. Their main duties include:
- Create AI models from scratch and explain to stakeholders (business users, product managers, etc.) how the models will produce results.
- I will analyze data statistically and interpret the findings to assist the company in making more informed business decisions.
- Create and manage products and infrastructure for Artificial Intelligence.
- Create infrastructures for transforming and ingesting data.
- Create a system to automate the infrastructure used by the data science team.
- Create machine learning applications to meet specific needs.
- Conduct experiments & tests using AI & machine learning.
- Train & update systems as needed.
- Turning machine learning models into APIs, so other applications can use them.
- Collaborate with other members of the AI team to coordinate tasks.
- Work with the Electronics & Robotics departments.
What is the salary of an AI Engineer?
Average salary for an AI engineer in U.S is $167,541, according to ZipRecruiter. Salaries can range from $90,000 to $304,500 & most usually fall between $142,500 & $173,000 with the highest earners making up to $216,500 a year.
In conclusion,
AI is no longer just a concept seen in movies – it’s now part of our daily lives. Artificial Intelligence Engineering has seen tremendous growth in recent years, offering numerous opportunities for those interested in pursuing a career in the field. The expansion of AI can be attributed to factors such as the advancement of machine learning, the widespread use of cloud computing resources, and the development of data collection, storage, and processing. AI technology has been used for a variety of purposes, including business operations, speech recognition, self-driving cars, games, and image analysis, and has greatly impacted the job market. While some are concerned about the potential impact of AI on jobs, the demand for qualified AI professionals continues to increase, making it an ideal time to pursue a career in the field. To become an AI engineer, one must possess the necessary educational prerequisites and skills, including proficiency in programming languages such as Python and R, understanding of statistics, calculus, and algebra, knowledge of algorithms and applied mathematics, proficiency in natural language processing, and a deep understanding of neural networks.
