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The extensive development of artificial intelligence (AI) and machine learning (ML) has forced the job market to adapt. The era of generalists in AI and ML has ended, and we are now in the age of experts.
It's difficult to gain more experienced experience, not to mention beginners.
That's why I've created this little guide to understand different AI and ML jobs.
What is AI & ML?
AI is a field of computer science that aims to create computer systems that demonstrate human-like intelligence.
ML is a subfield of AI that allows you to build and deploy models using algorithms, learn from data, and make decisions without programming explicit instructions.
AI & ML work
Due to the complexity of AI & ML and their different purposes, different jobs are applied in different ways.
This is the 10 jobs I talk about.
They all require AI & ML, where skills and tools can overlap, but each job requires some different aspects of AI & ML expertise.
Here is an overview of these differences:
1. AI Engineer
This role is specialized in developing, implementing, testing and maintaining AI systems.
Technical skills
The skills of core AI engineers revolve around building AI models, so programming languages and ML techniques are essential.
tool
The main tools used are Python libraries, tools for big data, and databases.
- Tensorflow, Pytorch – Uses neural networks and ML application creation Dynamic graph and Static graph calculations
- Hadoop, spark – Processing and analysis Big Data
- Scikit-Learn, Keras – implementation director and Unsurveillanced ML algorithms Constructed model including DL model
- SQL (e.g. postgreSql, mysql, SQL Server, Oracle), like NOSQL database mongodb (for Document-oriented data,for example, JSON– like the documentation) and Cassandra (Pillars and household data models Ideal for Time Series Data) – Storage and Management of Structured and Unstructured Data
project
AI engineers are working on automation projects and the following AI systems:
- Self-driving cars
- Virtual Assistant
- Healthcare Robots
- Production line robot
- Smart Home System
Types of interview questions
The interview questions reflect the skills required, so look forward to the following topics:
2. ML Engineer
ML engineers develop, deploy and maintain ML models. Their focus is Expanding and Tuning model in production.
Technical skills
The main skills of ML engineers are software engineering and advanced mathematics, apart from the usual suspects of machine learning.
tool
Tools ML Engineer's tools are similar to AI Engineers.
project
The knowledge of ML engineers is employed in these projects.
Types of interview questions
This is the focus of the interview, as ML is a central aspect of all ML engineer jobs.
- ML concept – ML basics, for example, types of machine learning, Overfittingand The weak
- ML Algorithm
- Coding questions
- Data Processing – The Fundamentals for Preparing Data for Modeling
- Model evaluation – Model Evaluation Techniques and Metricsincluding accuracy, accuracy, recall, F1 score, ROC curve
- Problem solving questions
3. Data Scientist
Data scientists collect and clean data and perform Exploratory Data Analysis (EDA) to better understand it. They create statistical models, ML Algorithmand visualizations for understanding and predicting patterns in data.
Unlike ML engineers, data scientists are more involved in the early stages of the ML model. They focus on discovering data patterns and extracting insights from them.
Technical skills
The skills used by data scientists focus on providing actionable insights.
tool
- Tableau, Power By – Data visualization
- Tensorflow, Scikit-Learn, Keras, Pytorch – Development, training and deployment of ML and DL models
- Jupyter Notebook – Interactive coding, data visualization, documentation
- SQL and NOSQL Databases – Same as ML Engineers
- Hadoop, spark – Same as ML engineers
- Panda, numpy, scipy – Data manipulation and numerical calculations
project
Data scientists are working on the same project as ML engineers, only before deployment.
Types of interview questions
4. Data Engineer
Develop and maintain data processing systems, build data pipelines, and ensure data availability. Machine learning is not their core job. However, you need to understand the fundamentals of ML since you are working with ML engineers and data scientists to ensure the availability of data in your ML model. Additionally, ML algorithms may be integrated into data pipelines for data classification and anomaly detection.
Technical skills
- Programming language (Python, Scala, Java, Bash) – Data manipulation, big data processing, scripting, automation, building Data PipelineSystem Processes and File Management
- Data Warehousing – Integrated Data Storage
- ETL (Extract, Convert, Load) Process – Building an ETL pipeline
- Big Data Technology – Distributed Storage, Data streamingAdvanced analysis
- Database Management – Data Storage, Security, and Availability
- ML – For ML-driven data pipelines
tool
project
Data engineers are working on projects that make data available in other roles.
- Building an ETL pipeline
- Building system for data streaming
- Supporting the deployment of ML models
Types of interview questions
Data engineers need to demonstrate knowledge about data architecture and infrastructure.
5. AI research scientist
These scientists are conducting research focused on developing new algorithms and AI principles.
Technical skills
- Programming language (Python, r) – Data analysis, Prototyping & AI model development
- Research Methodology – Experimental designhypothesis formulation and testing, and outcome analysis
- Advanced ML – Development and completion of algorithms
- NLP – Improved functionality of the NLP system
- DL – Improved functionality of the DL system
tool
- Tensorflow, Pytorch – Development, training, and deployment of ML & DL models
- Jupyter Notebook – Interactive coding, data visualization, and research workflow documentation
- latex – Scientific writing
project
They work on creating and advancement in the algorithms used.
Types of interview questions
AI research scientists must show practical things and Very powerful theoretical AI & ML knowledge.
- Theoretical Basics of AI & ML
- Practical Applications of AI
- ML Algorithms – Theory and Applications of Different ML Algorithms
- Methodology Basics
6. Business Intelligence Analyst
BI analysts analyze data, present actionable insights and present it to stakeholders via data visualization, reports, and dashboards. Business Intelligence AI is most commonly used to automate data processing, identify trends and patterns in data, and identify predictive analytics.
Technical skills
- Programming language (Python) – Data Queries, Processing, Analysis, Reporting, Visualization
- Data Analysis – Provide actionable insights for decision making
- Business Analysis – Identifying opportunities and optimizing business processes
- Data Visualization – Presents visual insights
- Machine Learning – Predictive analytics, anomaly detection, enhanced data insights
tool
project
The projects they are working on focusing on analysis and reporting.
- Churn analysis
- Sales Analysis
- Cost analysis
- Customer segmentation
- Process improvements, for example, inventory management
Types of interview questions
BI Analyst interview questions focus on coding and data analytics skills.
- Coding questions
- Data and Database Fundamentals
- Basics of Data Analysis
- Problem solving questions
Conclusion
AI & ML is a wide and ever-evolving field. As they evolve, so do jobs that require AI & ML skills. Nearly every day there is new job descriptions and specialization, reflecting the growing need for businesses to take advantage of the possibilities of AI and ML.
I discussed six jobs that you rated most interested in. But these are not the only jobs of AI and ML. There are so many more, and they will continue to come, so try to stay up to date.
Nate Rosidy Data Scientist and product strategy. He is also an analytics teaching adjunct professor, founder of Stratascratch and a platform that helps data scientists prepare interviews with real-world interview questions from top companies. Nate writes about the latest trends in the career market, provides interview advice, shares data science projects, and covers all SQL.
