Most of us are confused about these job Tittle – AI Software Engineer and Data Scientist. Both of the job titles are high paying in the industries. I have heard the same question in multiple communities. Consequently, I decided to write an article on this topic. Get the complete detail about the difference between Machine Learning Engineer or AI Engineer Software vs Data Scientist: Role and Responsibility. In order to develop larger intelligent software products, both roles are equally important. let’s explore –
AI Software Engineer (Machine Learning Engineer) Role and Responsibility –
An AI Engineer’s responsibility starts from creating a usable product for clients and customers where AI is involved. In order to simplify the role and responsibility of AI engineers, we can break it two parts – core and optional responsibilities.
AI Software Engineer core Role and Responsibility –
An AI engineer works closely with Data Scientist and performs the below task –
- Build Code Infrastructure – Basically, when data scientists work they usually build models on IDEs. When we need to integrate that with Products we have to solve so many problems. Now it is an AI engineer’s responsibility to create an easy deployable version of ML -Models using Docker like technologies. Basically packaging of ML model and its Integration into Products.
- Creating End Point API – Most of the data science model needs to be deployed as a web service. Now we can call these web service API from and end front end like – Mobile app or Web etc. So AI developer needs to create secure endpoint API for ML models if required.
AI Software Engineer optional Role and Responsibility –
These responsibilities are optional for AI Engineer –
- Build Machine Learning Model – Actually this is a core responsibility for data scientists. But In some organization, AI software Engineer has to provide end to end AI solution.
- Data Collection and building pipeline – For large projects where data volume is higher, Some time AI engineer has to perform data engineer’s job as well.
Who is full-stack AI engineer –
Someone who has all skills as mention 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 in entire projects. Generally, I have seen small organizations hire full-stack AI developers. On the opposite side, Big companies have a big army of developers. In MNC’s there will be specific persons for specific tasks. But the changing trend in the business and IT sector, Full stack developer, and AI engineer are in huge demand. It will be a trend with the growth of a startup is this era.
Data Scientist’s Role and Responsibilities –
In this section, nothing is new. All the optional responsibility for AI developers are most likely data science core responsibility. In fact, a data scientist has to perform following task –
1.Data science problem formulation
2. collect the relevant data
3. clean the data
4. Apply preprocessing steps like feature engineering over it.
5. split data set into training and testing set
6. Train the model
7.tune the model .etc
Usually, Data engineers have a very different task to data scientists but in some scenarios, a data scientist needs to fulfill both. In a similar way as AI software Engineer has to work end to end.
I think now there is a clear boundary between both Job role.
Are Big Data Technologies like ( Hadoop and Map Reduce etc ) must have?
No, neither it is must-have for data scientist nor for AI engineer as well. It is just good to have knowledge of both job roles. Actually It is only just to have for data engineers.
Truly speaking these are just boundaries. In real-time you will see engineers are cross-functional. People are transforming their profiles. I have also written a similar article – How a Java Engineer can Transform his career into Data Science | Java for Data Science? Still, we have tried to give an Imaginary view of these two job profiles in the AI industry. Well! Any article is not complete it gets a response from the reader. The positive response becomes our motivation and negative become the suggestions. In short please comment below if you have any queries or suggestions for the topic – AI Software Engineer vs Data Scientist: Role and Responsibility.
Data Science Learner Team
Join our list
Subscribe to our mailing list and get interesting stuff and updates to your email inbox.