Most of us are confused about these jobs Title - AI Software Engineer and Data Scientist. Both job titles are highly paying across industries. I have heard the same question in many communities. As a result, I decided to write an article on this topic. Machine Learning Engineer or AI Engineer Software vs Data Scientist: Get a complete idea about the difference between Role and Responsibility. To develop large intelligent software products, both roles are equally important. let's explore -
AI Software Engineer (Machine Learning Engineer) Role and Responsibilities -
The responsibility of an AI engineer starts with creating a useful product for customers and customers where AI is involved. To simplify the role and responsibility of AI engineers, we can break it down into two parts - core and optional responsibilities.
AI Software Engineer vs Data Scientist: Role and Responsibilities
AI Software Engineer Optional Roles and Responsibilities -
These responsibilities are optional for an AI Engineer -
Build machine learning models - in fact this is a core responsibility for data scientists. But in some organizations, AI software engineer has to provide end to end AI solutions.
Data Collection and Creation Pipeline - For large projects where the volume of data is high, sometime an AI engineer has to do the job of a data engineer as well.
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Who is a Full-Stack AI Engineer?
Someone who has all the skills as mentioned above. I mean who can work as a developer (AI software engineer) and data scientist in an organization is a full-stack AI engineer. They work as a one-man army throughout the projects. Generally, I have seen small organizations hire full-stack AI developers. Large companies, on the other hand, have a large army of developers. MNCs will have specialized individuals for specific tasks. But with the changing trends in the business and IT sector, there is a huge demand for full stack developers and AI engineers. It will be a trend in this era with the growth of a startup.
Role and Responsibilities of Data Scientist -
There is nothing new in this section. All optional responsibility for AI developers is the core responsibility of data science. In fact, a data scientist has to do the following -
1.Data Science Problem Formulation
2. Collect Relevant Data
3. Clear Data
4. Apply preprocessing steps like feature engineering on top of it.
5. Split the data set into training and test sets
6. Train the Model
7. Tune the model .etc
Typically, data engineers have a very different task to that of data scientists, but in some scenarios, a data scientist is required to fulfill both. Just like an AI software engineer has to work end to end.
I think there is a clear demarcation between the two job roles now.
Should there be Big Data Technologies like (Hadoop and Map Reduce etc.)?
No, it is neither necessary for Data Scientist nor for AI Engineer. It is good to have knowledge of both the job roles. Actually this is just for data engineers. But if you know any big data technology as a data scientist then you will be in high demand.
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conclusion -
To be honest, these are just limits. In real-time you will see that engineers are cross-functional. People are changing their profiles. I have also written a similar article - How a Java Engineer can turn his career into Data Science. Java for Data Science? Nevertheless, we have tried to give a hypothetical view of these two job profiles in the AI industry. Well! Any article that is not complete gets a response from the reader. Positive feedback becomes our motivation and negative becomes our suggestion. In short please comment below if you have any query or suggestion for the topic - AI Software Engineer vs Data Scientist: Role and Responsibility.
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