There has been a growing fear that AI would eat away jobs from every sector, including Software Services. Read this article of ours to know more: Is Artificial Intelligence a threat to Software Engineers?
While the fear is not completely unfounded, there is another side to the coin. The field of AI or Machine Learning is growing into an industry. A decade ago, there were only a couple of roles like AI Scientists or Data Scientists. Especially, the latter exploded after this article from Harvard Business Review. But, as the field evolved, newer roles kept emerging. Here are a few of them:
Horizontal Careers in Machine Learning and AI
Here are a few horizontal careers in Machine Learning and AI. They cut across domains and verticals alike.
Data Engineer
AI and Machine Learning stands on data. Hence, data extraction, cleansing and transformation assume primary importance. Data Engineers play an important role of managing data life cycle, to be used by the professional next in line i.e. Data Analyst.
Key Skills: SQL, Python, Spark, etc.
Data Analyst
Machine Learning or AI is about prediction or automation. Hence, you may argue that why do we Analysts? Let me put a counter question. Who determines what should be predicted or automated? It’s a Data Analyst who does that. By analysing data, they find opportunities to improve and/or automate business processes.
Key Skills: SQL, Data Visualization, Statistics, etc.
Data Scientist
Once a Data Analyst finds an opportunity, Data Scientists take over. They translate a business problem into a data problem, perform statistical analysis. Furthermore, they apply algorithms, build a model, and produce results. Thus, it is a versatile role requiring traits of an analyst and an engineer.
Key Skills: SQL, Python, Data Visualization, Statistics, Machine Learning/Deep Learning, etc.
Machine Learning Engineer
Once a Data Scientist builds a model, a Machine Learning Engineer takes it to production. They create and maintain the infrastructure to run and serve ML models at scale.
Key Skills: Machine Learning/Deep Learning, Software Engineering, Distributed Computing, etc.
MLOps Engineer
End of the day, Machine Learning code is a piece of software. Hence, it has a life cycle of its own. Thus, a new role is gaining traction i.e. MLOps engineer. Basically, they are DevOps, responsible to Machine Learning Lifecycle.
Key Skills: Machine Learning, Source control, CI/CD, Software Engineering principles, etc.
Vertical Careers in Machine Learning and AI
Having said that, careers in machine learning and AI are not only distributed horizontally but also vertically. Key among them are Forecasting, Recommender Systems, Natural Language Processing, Computer Vision, Conversational AI. Hence, roles like NLP Scientist/Engineer, Computer Vision Scientist/Engineer etc. are emerging. And, all the above roles apply to each vertical.
So in conclusion, I would say that with new roles and trends Careers in Machine Learning and AI look exciting. This is an exciting industry to look out for with great compensations.
