Introduction
I’m going to talk about technical and non-technical roles available in the AI industry, along with skills required for them and salaries. Building a career in AI requires a lot of hard work. So if you’re looking for a shortcut, something like “learn AI in 15 days and get 5x salary hike,” then please leave this video because that is not going to happen.
Let’s start with technical roles. I’m showing five career options available when it comes to technical roles, and each of these roles requires technical and non-technical skills. We call technical skills as tool skills and non-technical skills such as communication as core skills.
Data Scientist
The first role is Data Scientist. Data Scientist role is sort of like extracting butter out of buttermilk. Here the buttermilk is data. On that, as a Data Scientist, you can use descriptive analytics to extract butter or to extract data insights, or you can train a machine learning model to perform predictive analytics.
On the screen, I am showing the tool skills and core skills required for a Data Scientist role. Here, you need a strong foundation in programming languages such as Python or R. You obviously need to have strong mathematics and statistics fundamentals, and you need to be good in communication because you’ll be dealing with business stakeholders a lot, and you need to convey your data insights.
If you want to learn Data Scientist skills using free learning resources, then refer to this Data Science Roadmap video on YouTube, where I have given week-by-week study plan using free learning resources and checklist. Now these skills will not only upskill you, they will make you job-ready. I can say this with confidence because in my own companies, Attick and CodeBasics, I hire Data Scientists, and when I hire them I look for these skills. I have many senior Data Scientist friends who are interviewers in many big tech companies, and these people also look for these skills.
AI Engineer
The second career role is AI Engineer. Sometimes people call it ML Engineer. I have seen job postings where they use a term AI/ML Engineer. They all mean the same thing.
AI Engineer is a combination of Data Scientist and Software Engineer. You will use your data science skills such as math, statistics knowledge, Python coding, ML algorithm knowledge to build and train machine learning models. And then you will use your software engineering skills to deploy these models into production and integrate them with rest of the software apps in your organization.
This diagram on the screen is showing tool skills and core skills required for AI Engineer role. And once again, if you want to learn these skills using free learning resources, then refer to this particular YouTube video where I have given a complete roadmap with week-by-week study plan along with free learning resources and checklist.
This video got a tremendous response in first two weeks, and you can see comments such as this particular person from Vietnam. He’s a 30-year-old bank employee. He used free learning resources on YouTube to learn AI skills, and he got a job as an AI Engineer.
Salaries for Data Scientist and AI Engineer are kind of same. I’m showing a broad range both in India and US for Data Scientist and AI Engineer roles. Now the exact salary that you get depends on three factors: your skills and experience, your location, and the company that is hiring.
I would suggest that you go to LinkedIn Jobs and search for AI Engineer jobs, and you will find the skills that each of these jobs are asking for. You will also find that many times people use a variety of career roles such as GenAI Engineer, AI Developer, AI Software Engineer, etc. They all mean essentially the same thing. There is slight change in requirements. For example, for GenAI Engineer, you will be focusing mainly on GenAI things like LLMs, LangChain framework, etc. But essentially you are working as an AI Engineer.
NLP Engineer, CV Engineer
The next two roles are NLP Engineer and Computer Vision Engineer. These are specialized AI Engineer roles. Let’s say if you have a general doctor (general physician), that doctor can decide to become a heart doctor or a lung doctor. In similar fashion, an AI Engineer can decide to do specialization in either Natural Language Processing; in that case, they will become NLP Engineer, or they can do specialization in Computer Vision fundamentals and they become Computer Vision Engineer.
For NLP Engineer, you need to have strong NLP fundamentals and knowledge of libraries such as NLTK, SpaCy, etc. For Computer Vision, you need to have strong fundamentals of Computer Vision and know-how of libraries such as OpenCV, YOLO, etc.
The salary range for NLP and Computer Vision is same as AI Engineer. Obviously, depending on company and their exact requirements, these salaries would vary. I have seen many companies who require PhD or Master’s degree when they are hiring either NLP or CV Engineers because many times they have this specialized research roles where they require a lot of academic knowledge.
When I was working in Bloomberg, we had an AI department where more than 150 AI Engineers were PhDs. Now, Bloomberg is a tech company such as Google, Facebook, etc., where they build their own proprietary software and for that they require PhDs. Majority of the roles do not require PhDs or Masters, so don’t worry about it. You have to think about PhD when you go for a very specialized role such as AI Research Engineer.
On the screen, you can see tool skills and core skills required for both NLP Engineer and Computer Vision Engineer.
ML Ops Engineer
Alright, the next one is MLOps Engineer. You might have heard about DevOps Engineer in the field of software engineering. So MLOps Engineer is nothing but he or she is a DevOps Engineer and they are doing DevOps for Machine Learning projects. That is the only difference.
So they will be involved in setting up CI/CD pipeline for ML projects. They will be using tools such as Amazon Sagemaker, which is Amazon’s platform for doing Machine Learning in the cloud. And they will have know-how of specialized tools such as MLflow, Kubeflow, etc. As part of their MLOps job, they will be using Kubernetes, Docker, etc., to deploy ML solutions.
This role is perfect for DevOps Engineers who are working in traditional software engineering field. They already know the DevOps principles. Now they need to know few more tools such as MLflow, they need to know some ML principles, and then they can easily transition to this role.
I’m showing salary range both in India and US. And by the way, these salary ranges we have come up with based on Glassdoor, based on research on internet, and based on our internal network. In my network, there are a lot of folks who are working as MLOps Engineer, so these salary ranges are based on that knowledge.
Data Analyst & Data Engineer (Mentioned in Comparison)
Now you might be wondering, how about Data Analyst and Data Engineering role? In order to do AI, you need data, and these two career roles are centered around data.
Data Engineer role especially is very important for any AI project because if Data Engineer is not building a data pipeline and making data available for AI project, then Data Scientist or AI Engineer will not be able to do their work.
To give you analogy, Data Scientists and AI Engineers are like chefs who are making pizza, but for pizza you need ingredients — cheese, vegetables, etc., and Data Engineer is a person who provides those raw ingredients. So if they don’t provide raw ingredients, your chef, who is Data Scientist or AI Engineer, will not be able to make pizza. Hence, I’m seeing a huge rise in Data Engineering jobs, so learning Data Engineering can be a very good career option.
Similarly, Data Analyst performs descriptive analytics. They build dashboards in Power BI, Tableau, etc. And I’ve seen many industry projects where you do not need AI. Just by doing descriptive analytics, you can get a lot of value. Therefore, the future for Data Analyst is also bright.
If you want to learn these skills using free learning resources, then I have another roadmap for Data Analyst where I have given all free learning resources and week-by-week study plan.
✅ Now Non-Technical Roles
AI Product Manager
Now let us discuss non-technical roles. The first one is AI Product Manager. Recently, there was a news that Netflix is hiring AI Product Manager for a salary of $900,000 a year. In terms of Indian rupees, that is more than 7 Cr rupees. So you can clearly see that there is a huge demand for AI Product Managers and they get paid really well.
On the screen, you are seeing a diagram with both tool skills and core skills. In tool skills, you need to have know-how of product management tools such as Jira and Asana, prototyping tools such as Figma, Sketch, roadmapping software such as Roadmunk, ProductPlan, and basics of Excel and Power BI are always useful because you can do quick analysis for your project.
You need to be very solid in terms of your core skills which are business understanding, stakeholder management, working with technical teams, and for all of that you obviously need communication.
Once again, refer to the diagram on the screen and I’m going to attach a PDF file with core and tool skills for every single role that we have mentioned in this video.
I’m showing the salary range both in India and US. Note that in the US this role is very well established, whereas in India it is still evolving as of this recording in March 2024.
AI Ethics Executive
The next one is AI Ethicist or AI Ethics Executive. There is lot of talks going on in terms of regulating AI, protecting private information, etc. As an AI Ethics Executive, you will be working with AI teams and guiding them on AI ethics and government rules, compliance, etc.
Anyone who has a background as a lawyer or any regulatory background will find this role to be exciting. And here are the salaries both in India and US.
AI Sales Representative
The last non-technical role is AI Sales Executive. Any person who is working as a salesperson in any industry might find this role to be interesting. Here, you need to build know-how on AI and ML, maybe get familiar with some AI and ML technology and terms, and then you can use your existing sales skills to become AI Sales Representative.
Talking about tool skills, you need to have know-how of proposal and contract tools such as DocuSign, Adobe Sign, etc., and you need to be familiar with CRM software such as Salesforce, HubSpot. Refer to this diagram for complete set of tool and core skills.
Talking about salaries, Sales Executives usually get fixed salary and they also get commission. Sometimes the money that they earn via commission is more than their fixed salary.
In Attick Technologies, we have a sales partner here in Texas, USA. That person made more than $350,000 in the year 2023 referring projects to us. So what they do is they get the project, they do some kind of client relationship management, and my team in India executes all these projects. So you can see that there is a huge financial potential in this AI Sales Representative role.
Conclusion
We just finished discussing about technical and non-technical roles and the immediate question you have is: how do I decide which role is the best one for me? I am going to publish a detailed article on that. Stay tuned.