Businesses can benefit from artificial intelligence (AI) in multiple ways and so can end-users. From chatbots to music recommendations, artificial intelligence is behind it all. Advanced automation is heavily dependent on AI as well, as seen in various manufacturing processes. Let us take the example of automated maintenance where AI evaluates the condition of machines and then recommends repairs before a probable breakdown. RPA or robotic process automation is also AI-based. NLP or natural language processing is another example that helps machines interpret human language. The use of NLP can be seen in multiple AI implementations such as chatbots and smart translators. These AI solutions require a lot of research and development. This is where an artificial intelligence engineer comes into the picture. AI engineers understand the various business requirements and then help companies build these AI solutions.
AI engineers help design and build models that power these AI systems, thus being crucial for implementing real-world machine learning applications or for automation. Developers working with AI are also in charge of designing the architecture of the AI system as well as the data model that will sustain the system.
Who are Artificial Intelligence Engineers?
AI engineers are tasked with building models to deploy AI-based applications. For example, in order to satisfy a requirement, a business might need to incorporate AI systems that can predict anomalies. It is the job of AI engineers to use ML techniques and neural networks in order to achieve this. The research and evaluation that is required before designing the system are also carried out by AI engineers. Unlike traditional ML engineers who use datasets to carry out current objectives, AI engineers must ensure that AI systems keep functioning with external input. ML engineers are focused on current objectives while AI engineers ensure that the system can sustain itself and function without supervision.
AI engineers are experts in AI reasoning, logic, probability analysis and uncertainty. These engineers use ML concepts for building solutions and then monitor how the system (solution) functions. This allows them to identify the flaws in the system and keep improving it. AI engineers also have to take care of data processing and data mining that is involved with training the AI system. They must also design the entire data system and infrastructure that will sustain the AI project. Thus, AI engineers must be fluent in both data science and programming. Speech recognition is another example of an application of AI that is built using NLP, machine learning concepts, analytics and massive datasets. Unlike traditional developers, AI engineers must be fluent enough to work with all of these together.
Here are some additional job roles of an AI engineer:
- Working with data scientists for sourcing data, research and identifying data requirements
- Collaborating with programmers and system design specialists to build the architecture of the AI system
- Managing the infrastructure of the development pipeline
- Statistical analysis
- Data transformation
- Using external input for AI models
- Designing and sustaining the AI system
- Testing and debugging
- Integrating APIs in order for AI systems to interact with external applications
Artificial Intelligence Engineer Salary
According to Glassdoor, the average artificial intelligence engineer salary in India is Rs 9,00,000 per annum. Meanwhile, AI engineers earn an average of $1,20,000 annually in the United States. PayScale reports similar numbers with $1,25,000 per annum as the global average for AI professionals.
The artificial intelligence engineering scope in India is great. Both freshers and professionals who wish to gain entry into this domain can expect handsome salaries. Meanwhile, the sky is the limit for veterans with massive experience as they can become the director or vice president of an AI project.
Skills Required to Become an Artificial Intelligence Engineer
Here are the skills that are necessary for artificial intelligence engineer jobs:
- Mathematics: AI models are based on algorithms, calculus and linear algebra. Thus, being fluent in mathematics is a must.
- Statistics: AI-based systems are heavily dependent on probability models, statistical analysis and other models that rely on statistics. For example, Naive Bayes or Gaussian Mixture models.
- Programming: In order to design AI systems, learning programming languages such as Python, C++ or even Java is a must. One can also learn Scala or R. AI engineers need to write programs and test systems.
- Analytics: AI engineers must have analytical skills in order to interpret valuable data. In order to properly build a functional AI model, one needs to be able to analyse data, gain insights and then identify solutions. One needs to be able to work with large datasets as well.
- Communication: This is important in order to understand business requirements from stakeholders. Business communication comes in handy when reporting about progress or new developments.
- Big data technologies: Due to the need for working with large volumes of data in real-time, AI engineers must be able to work with technologies such as Hadoop, Spark or MongoDB.
- Artificial intelligence engineering subjects: AI engineering concepts such as machine learning and artificial neural networks are absolutely necessary. These are a foundational part of an artificial intelligence engineer’s education.
- DBMS and cloud computing: Like big data technologies, knowing traditional DBMS is a must along with a good understanding of SQL. Cloud computing is also required when working with cloud-based AI systems.
- Collaboration: One needs to work with a team when involved with AI-based projects. AI engineers need to cooperate with many others from other domains to make the AI product successful. For example, AI engineers work hand in hand with data scientists.
- Logic and critical thinking: This is extremely important in order to come up with solutions and innovative methods to solve problems.
Artificial intelligence engineers help convert intelligence solutions into practical applications. Even though machine learning is an important part of AI, artificial intelligence engineers are not just ML engineers but also AI architects. AI engineering requires systematic research and creativity, thus, one can expect to do a lot of brainstorming as an AI professional.