Analytics Insight explains the difference between big data engineers vs Artificial intelligence engineers.

'A domain for the nerds,' this is what technology was called in the late 1900s. However, a lot of things changed in the 21st century. In the digital world, we welcome hundreds of new Artificial intelligence-powered tools and solutions every day. Owing to the drastic surge in the implementation of artificial intelligence, the technology market has opened its door to more jobs. On the other hand, big data is also bringing many organizational changes to companies. Big data was previously seen as useless content occupying most of the memory in data centers. Fortunately, when technology evolved and became advanced, businesses realized the importance of big data and used it to get data-driven decisions. Following the upsurge in big data and artificial intelligence, two profiles namely big data engineer and artificial intelligence engineer took center stage. According to LinkedIn's 2020 Emerging Jobs report, artificial intelligence engineers and data-related jobs continue to make a strong showing as the top emerging job roles for 2020 with 74% annual growth in the past four years. Big Data Engineering vs Artificial Intelligence Engineering is two data job roles that are often used interchangeably due to their overlapping skillset but are actually different. In this article, Analytics Insight explains the difference between big data engineers vs AI engineers and helps you choose the right career.

Definition 

Big data engineer: Big data engineering is a branch of data science that deals with the practical applications of data analysis and collection. A big data engineer is in charge of the design and development of data pipelines. They intensely work to collect data from various sources and give it for further processing to analysts and data scientists. Even though the profile is not directly connected to business teams and business decision-making, it centers on developing systems for better flow and access to information.

Artificial intelligence engineer: An artificial intelligence engineer is someone who works with algorithms, neural networks, and other tools to advance the field of artificial intelligence. They deal with artificial intelligence problems and solve them. Artificial intelligence engineers develop techniques and use them in commerce, science, and other fields. They must be able to extract data efficiently from a variety of sources, design algorithms, build and test machine learning models, then deploy those models to create AI-powered applications capable of performing complex tasks.

Roles and responsibilities

Big data engineer: A big data engineer has to design, develop, construct, install, test, and maintain the complete data management and processing system. Their key role is to seek the raw data and make it usable for other professionals. Without a big data engineer, the company can't collect data from various sources. Not just collection, they also engage in managing the collection of data and handles its storage, and process it for further use. Some of the other routine responsibilities of a big data engineer are as follows,

• Build highly scalable, robust, and fault-tolerant systems to manage high volumes of data.
• To introduce new big data management tools and technologies to stay ahead in the race.
• Explore various choices of data acquisitions and try out new ways to use existing data.
• Create a complete solution by integrating a variety of programming languages and tools together.
• Employ disaster recovery techniques in case of mishaps.

Besides the basic responsibilities, big data engineers are expected to be well-versed in a set of technological aspects. They should have in-depth knowledge of big data technology and communicate the ideas within and out of the team. In order to carry out the task, they should be experts in the following context.

• Basic knowledge about Java, data structuring, and big data.
• Familiarity with NoSQL solutions, Cassandra, HIVE, CouchDB, and HBase.
• Experience in analytics, OLAP technologies, and more.

Artificial intelligence engineer: Besides creating techniques, artificial intelligence engineers are assigned other organizational responsibilities as well. In order to integrate their technique across the enterprise, artificial intelligence engineers must be able to overcome the unique challenges that result from combining the logic of traditional business applications with the learned logic of machine learning models. Some of the other responsibilities are as follows,

• Build artificial intelligence and machine learning models, then convert the machine learning models into application program interfaces (APIs) so that other applications can use them.
• Help stakeholders understand the output yielding.
• Set up and manage AI product infrastructure and the automation of the infrastructure used by an organization's data science team.
• Conduct statistical analysis and interpret the results to help organizations drive data decisions.

So, what should you choose as your career option?

According to the World Economic Forum, artificial intelligence was anticipated to create over 58 million jobs by the end of 2020. As we are already in the middle of 2021, artificial intelligence engineering and big data engineering are seeing a sweeping demand rise in the job market. But while choosing a career between these two, you should validate your interest and preferences. If you are someone who is solely interested in data and big data management, it is safe to say that you are destined to work as a big data engineer. If you like coordinating with other teams and want to work out of the clustered data, then artificial intelligence engineering will better suit you.