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The massive development of artificial intelligence (AI) and machine learning (ML) has forced the job market to adapt. The era of AI and ML generalists is over and we have entered the era of specialists.
It can be difficult for even the most experienced to navigate, let alone beginners.
This is why I created this little guide to understand the different professions in AI and ML.
What are AI and ML?
AI is a field of computer science that aims to create computer systems with human-like intelligence.
ML is a subfield of AI that uses algorithms to create and deploy models that can learn from data and make decisions without explicit instructions being programmed.
AI and ML Jobs
The complexity of AI and ML and their diverse purposes mean that different jobs apply them differently.
Here are the ten jobs I’m going to tell you about.
While they all require AI and ML, with sometimes overlapping skills and tools, each job requires a distinct aspect of AI and ML expertise.
Here’s an overview of those differences.
1. AI Engineer
This role specializes in the development, implementation, testing and maintenance of AI systems.
Technical skills
The core skills of AI engineers revolve around creating AI models, so programming languages and ML techniques are essential.
Tools
The main tools used are Python libraries, big data tools and databases.
- TensorFlow, PyTorch Torch – creation of neural networks and ML applications using dynamic graphics And static graph calculations
- Hadoop, Spark – processing and analysis big data
- Scikit-learn, Keras – implementation supervised And unsupervised ML algorithms and construction models, including DL Models
- SQL (eg, PostgreSQL, MySQL, SQL Server, Oracle), NoSQL databases like MongoDB (For document-oriented dataFor example, JSON-as documents) and Cassandra (column family data model excellent for time series data) – storage and management of structured and unstructured data
Projects
AI engineers work on automation projects and AI systems such as:
- Autonomous vehicles
- Virtual Assistants
- Health robots
- Production line robots
- Smart Home Systems
Types of Interview Questions
Interview questions reflect the skills required, so expect the following topics:
2. ML Engineer
ML engineers develop, deploy, and maintain ML models. Their goal is deployment And production adjustment models.
Technical skills
The main skills of ML engineers, besides the usual machine learning skills, are software engineering and advanced mathematics.
Tools
ML engineers’ tools are similar to those of AI engineers.
Projects
ML engineers’ knowledge is used in these projects:
Types of Interview Questions
ML is the core aspect of every ML engineer’s job, so it’s what their interviews are about.
- ML Concepts – Fundamentals of ML, e.g. types of machine learning, overlearningAnd underfitting
- ML Algorithms
- Coding Questions
- Data Processing – Fundamentals of Preparing Data for Modeling
- Model evaluation – model evaluation techniques and measuresincluding accuracy, precision, recall, F1 score and 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 Algorithmsand visualizations to understand patterns within the data and make predictions.
Unlike ML engineers, data scientists are more involved in the initial stages of the ML model; they focus on discovering data patterns and extracting insights from them.
Technical skills
The skills used by data scientists are aimed at providing actionable insights.
Tools
- Painting, Power BI – data visualization
- TensorFlow, Scikit-learn, Keras, PyTorch Torch – develop, train, deploy ML & DL models
- Jupyter Notebooks – interactive coding, data visualization, documentation
- SQL and NoSQL databases – same as ML engineer
- Hadoop, Spark – same as ML engineer
- pandas, Numpy, SciPy – data manipulation and numerical calculation
Projects
Data scientists work on the same projects as ML engineers, just in the pre-deployment stages.
Types of Interview Questions
4. Data Engineer
They develop and maintain data processing systems and build data pipelines to ensure data availability. Machine learning is not their main job. However, they collaborate with ML engineers and data scientists to ensure data availability for ML models, so they must understand the fundamentals of ML. In addition, they sometimes integrate ML algorithms into data pipelines, for example for data classification or anomaly detection.
Technical skills
- Programming languages (Python, Ladder, Java, Hit) – data manipulation, big data processing, scripting, automation, creation data pipelinesmanagement of system processes and files
- Data Warehousing – integrated data storage
- ETL (Extract, Transform, Load) process – construction of ETL pipelines
- Big Data Technologies – distributed storage, Streaming dataadvanced analytics
- Database management – data storage, security and availability
- ML – for ML-driven data pipelines
Tools
Projects
Data engineers work on projects that make data available for other roles.
- Creating ETL Pipelines
- Building systems for data streaming
- Assistance with ML model deployment
Types of Interview Questions
Data engineers must demonstrate knowledge of data architecture and infrastructure.
5. AI Scientist
These scientists conduct research focused on developing new AI algorithms and principles.
Technical skills
- Programming languages (Python, R) – data analysis, Prototyping & deployment of AI models
- Research methodology – experience designformulation and testing of hypotheses, analysis of results
- Advanced ML – algorithm development and refinement
- NLP – Improving the Capabilities of NLP Systems
- DL – Improving the capabilities of DL systems
Tools
- TensorFlow, PyTorch Torch – develop, train and deploy ML & DL models
- Jupyter Notebooks – interactive coding, data visualization and documentation of research workflows
- Latex – scientific writing
Projects
They work on the creation and advancement of algorithms used in:
Types of Interview Questions
AI researchers must be pragmatic And very strong theoretical knowledge in AI and ML.
- Theoretical foundations of AI and ML
- Practical application of AI
- ML Algorithms – Theory and Application of Different ML Algorithms
- Methodological foundations
6. Business Intelligence Analyst
BI analysts analyze data, uncover actionable insights, and present them to stakeholders through data visualizations, reports, and dashboards. AI in business intelligence is most commonly used to automate data processing, identify trends and patterns in data, and perform predictive analytics.
Technical skills
- Programming languages (Python) – querying, processing, analysis, reporting, visualization of data
- Data Analytics – Providing Actionable Insights for Decision Making
- Business analysis – identify opportunities and optimize business processes
- Data visualization – visual presentation of information
- Machine learning – predictive analytics, anomaly detection, enhanced data insights
Tools
Projects
The projects they work on focus on analysis and reporting:
- Unsubscribe Rate Analysis
- Sales analysis
- Cost analysis
- Customer segmentation
- Process improvement, e.g. inventory management
Types of Interview Questions
BI analyst interview questions focus on coding and data analysis skills.
- Coding Questions
- Data and databases: fundamentals
- Fundamentals of Data Analysis
- Problem Solving Questions
Conclusion
AI and ML are vast and constantly evolving fields. As they evolve, so do the jobs that require AI and ML skills. Almost every day, new job descriptions and specializations emerge, reflecting the growing need for companies to harness the possibilities of AI and ML.
I’ve mentioned six careers that I think you’ll be most interested in. However, these aren’t the only careers in AI and ML. There are many more, and they’ll keep coming, so try to stay up to date.
Nate Rosidi Nate is a data scientist and product strategist. He is also an adjunct professor of analytics and the founder of StrataScratch, a platform that helps data scientists prepare for their interviews with real interview questions from top companies. Nate writes about the latest job market trends, gives interview tips, shares data science projects, and covers all things SQL.