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What are the Roles and Responsibilities of an Artificial Intelligence Engineer?
Published
01st Apr, 2024
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Today, over 77% of devices use some form of AI, whether to switch on lights, reply to text messages, or for complex tasks like detecting anomalies or analyzing user behavior. On its way to becoming the ‘new normal,’ AI is poised to have a ubiquitous presence in our daily lives.
I know that it takes many masterminds to design and develop these applications. However, I can give you an insight into my part as an AI engineer in the process. Primarily, I help develop AI-powered tools and applications. This requires proficiency in developing software, working with data, and programming. That being said, I did not initially start as an AI engineer. To get this position, I enrolled in a comprehensive AI certification to land a role as an AI engineer.
So, let me help you with my experience and knowledge of an AI engineer’s roles and responsibilities. This article will discuss all the skills required to become an AI engineer.
What is Artificial Intelligence?
AI is the science of simulating human intelligence by using machines, software, and networks of complex algorithms to carry out specific tasks. These could range from reading, comprehending, generating code, detecting gunshots, and alerting authorities to being a stock broker, predicting the weather, and creating art. It's safe to say that AI can take on anything.
This is done by developing programs that train on existing data to learn and perform like humans.
What Does an AI Engineer Do?
AI engineers are responsible for developing, programming, and training complex algorithms that can mimic human intelligence while carrying out specific tasks. As AI is a vast domain, an AI engineer must be familiar with multiple processes, including, but not limited to, software development, data science & engineering, programming, and testing.
Their work aligns with that of a data engineer because AI is a data-heavy science, i.e., it requires massive volumes of data to train on. While AI engineers are expected to be experts at coding and programming, they are often not required to work on code pertaining to data-related operations (mining, annotation, cleaning, etc). Instead, their work begins once this data is extracted and ready to be used in creating and testing machine learning models and APIs.
Artificial Intelligence Engineer Roles and Responsibilities
Let’s explore the roles and responsibilities of AI engineers at different levels of experience and expertise.
Entry-level (0-2 years of experience)
Entry-level AI engineers are required to assist in AI application development by overseeing fundamental parts of the process. While the exact AI engineer responsibilities depend on where you work and what you work on, some fundamental ones include
- Working on the application backend with programming languages like Python, Lisp, JavaScript, Scala, etc.
- Managing databases (both SQL and NoSQL),
- Implementing application architectures using Docker,
- Running and evaluating existing AI models, etc.
- Working with LLMs (large language models) to solve real-world problems, etc.
Average Salary: An entry-level AI engineer makes over $117,377 (Source: Springboard) annually.
Mid-level (3-5 years of experience)
AI engineers with over three years of experience are expected to partake in more responsibilities throughout the AI application development process. Besides the fundamental roles of an AI engineer, they are responsible for
- Algorithm development: designing and implementing ML algorithms to address business challenges.
- Advanced data processing and feature engineering: to fine-tune the input data.
- Integrating the AI solutions into production systems and applications.
- Collaborate with product managers, software engineers, and data scientists.
Average Salary: An AI engineer at a mid-to-senior level earns $138,506/year (Source: Springboard) on average.
Senior-Level (more than 5-6 years)
Senior artificial intelligence roles and responsibilities cover the entire AI-powered SDLC (software development life cycle). During the process, they’re responsible for:
- Reviewing project requirements and collaborating with product managers, engineering managers, scrum masters, and the client.
- Creating solution architecture that can be scaled to global requirements.
- Algorithm development - a critical and lucrative segment of AI engineering.
- Grasping a thorough understanding of the product-business logic up to an SME level.
Please note that all the above-mentioned duties are in addition to an AI engineer's fundamental roles and responsibilities, i.e., programming applications, managing data and databases, evaluating models, etc.
Average Salary: A senior AI engineer earns $159,000 on average. However, it may go as high as $203,000 with proficient skills.
What makes you a good artificial intelligence engineer?
To become proficient, your AI engineer roles and responsibilities require you to have a diverse set of skills, including both hard and soft skills.
Hard Skills
Some hard skills you must have as an AI engineer:
- Proficiency in programming languages: Mastering programming languages like Python, Prolog, LISP, Java, C++, etc., is important. These languages are widely used in AI and ML applications to create algorithms and optimize and deal with data.
- UNIX Tools: UNIX tools is a group of multi-tasking operating systems. Being familiar with UNIX is paramount for an AI engineer, as a significant part of processing data is done on Linux-based systems.
- Algorithms, models, and libraries: AI engineers must have experience with libraries like Spark MLlib, Scikit-learn, Theano, TensorFlow, etc. They should also have an understanding of learning procedures, hyperparameter tuning, and model selection. They must have a grasp of AI models like decision trees, K-nearest neighbors, random forests, SVM, Naive Bayes, etc.
- Deployment and DevOps: AI engineers also orchestrate and deploy AI applications. Hence, they must know how to use tools like Kubernetes and Docker to manage configurations, build containers, etc.
- AI Security: They must closely follow all security updates and potential vulnerabilities. As an AI engineer, it is a part of your responsibility to ensure the integrity, confidentiality, and availability of your input and output data.
Joining an Artificial Intelligence Bootcamp can give you a better understanding of all the hard skills you must hone to become a proficient AI engineer.
Soft Skills
Employers also consider your cultural fitness and soft skills as an AI engineer to ensure you’re a good fit for their organization. These skills include:
- Critical thinking and problem solving: An AI engineer must be able to find new solutions for existing problems. For that, you must be creative and flexible, examining different situations to determine the best possible solution.
- Interpersonal skills: AI engineers often work with other people, such as data engineers, data scientists, programmers, and product managers. This calls for them to have interpersonal skills like empathy, understanding, patience, etc.
- Communication: As an AI engineer, you must be able to communicate effectively while simplifying complex information so that even non-technical stakeholders can understand. This includes all verbal, written, and body language cues.
- Adaptability: AI is an evolving field. In such an environment, you must be able to accept change and be a lifelong learner to keep pace with how things are done.
- Time management: Working on projects with a tight deadline is common for AI engineers. This is why you must have strong time management skills and always stay focused to ensure you deliver the deliverables on time.
Artificial Intelligence Job Requirements
Before applying for an AI engineer’s role, you must investigate jobs in artificial intelligence. The most prominent AI job roles include principal scientist, AI engineer, machine learning engineer, computer vision engineer, data scientist, statistician, etc. Let’s look at the typical job requirements for all AI-based roles.
Educational Requirements:
- A Bachelor’s degree in a related field like data science, computer science, IT, or statistics.
- A Master’s degree (or an equivalent post-graduate) in statistics, mathematics, cognitive science, or computer science.
- Additional certifications or diplomas related to AI.
Technical Skills:
As mentioned in the section above, AI engineers must thoroughly understand AI and ML algorithms and models, know programming languages like Python, LISP, Scala, etc., and be familiar with different AI security vulnerabilities.
Soft Skills:
They must also be team team-player, be able to communicate complex information precisely, think critically, and manage deliverables within tight deadlines.
Experience:
Having some experience in leveraging AI strategies to real-world problems and building AI models can add a feather to your cap. But if you are only starting out as an AI engineer, you can also use some additional certifications or projects as a testament to your knowledge and skills. Maintaining a portfolio of projects you work on is another way to keep track of your work.
Conclusion
AI is a vast and developing field, with hundreds of advancements and roles coming up. As per predictions, AI’s economic impact could be staggering, reaching up to $15.7 trillion by 2030! These numbers are only proof of the limitless potential that artificial intelligence holds! If you’re interested in AI technology and want to pursue a career, this is the time to explore more and start gaining professional knowledge and experience. Enrolling in certifications is an excellent way to do this; you can check out KnowledgeHut’s AI certification to get a head start.
Frequently Asked Questions
1. What are the benefits of AI?
The exact utility depends on the application and industry where AI is being used. However, AI has many benefits, like reducing human error, providing digital assistance, making unbiased decisions, automating repetitive tasks, finding new innovations, scaling operations, providing 24x7 functionality, and many others.
2. What is the demand for artificial intelligence engineers?
AI is expected to continue taking center stage in several industries and sectors. More tech adoption is expected in MSMEs (micro, small, and medium enterprises), with salaries catching pace with bigger players. Consequently, AI engineers can expect more opportunities across several industries. More jobs will also open up in cybersecurity, cloud computing, IT servicing, and automated sales and marketing.
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