Beginner's Guide to Careers in AI and Machine Learning

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The widespread development of artificial intelligence (ai) and machine learning (ML) has forced the labour market to adapt. The era of generalists in ai and ML is over and we have entered the era of specialists.

It can be difficult for even the most experienced to find their way, and even more so for beginners.

That's why I created this little guide to understanding the different ai and ML jobs.

What are ai and ML?

ai is a field of computer science that aims to create computer systems that exhibit human-like intelligence.

<img decoding="async" class="aligncenter size-full wp-image-178527" alt="ai vs Machine Learning ” width=”5818″ height=”3153″ srcset=”https://technicalterrence.com/wp-content/uploads/2024/08/1723723294_738_Beginner39s-Guide-to-Careers-in-AI-and-Machine-Learning.png 5818w, https://www.kdnuggets.com/wp-content/uploads/Rosidi_careers_in_AI_and_ML_2-300×163.png 300w, https://www.kdnuggets.com/wp-content/uploads/Rosidi_careers_in_AI_and_ML_2-1024×555.png 1024w, https://www.kdnuggets.com/wp-content/uploads/Rosidi_careers_in_AI_and_ML_2-768×416.png 768w, https://www.kdnuggets.com/wp-content/uploads/Rosidi_careers_in_AI_and_ML_2-1536×832.png 1536w, https://www.kdnuggets.com/wp-content/uploads/Rosidi_careers_in_AI_and_ML_2-2048×1110.png 2048w” data-lazy-sizes=”(max-width: 5818px) 100vw, 5818px” src=”https://technicalterrence.com/wp-content/uploads/2024/08/1723723294_738_Beginner39s-Guide-to-Careers-in-AI-and-Machine-Learning.png”/><img decoding="async" class="aligncenter size-full wp-image-178527" src="https://technicalterrence.com/wp-content/uploads/2024/08/1723723294_738_Beginner39s-Guide-to-Careers-in-AI-and-Machine-Learning.png" alt="ai vs Machine Learning ” width=”5818″ height=”3153″ srcset=”https://technicalterrence.com/wp-content/uploads/2024/08/1723723294_738_Beginner39s-Guide-to-Careers-in-AI-and-Machine-Learning.png 5818w, https://www.kdnuggets.com/wp-content/uploads/Rosidi_careers_in_AI_and_ML_2-300×163.png 300w, https://www.kdnuggets.com/wp-content/uploads/Rosidi_careers_in_AI_and_ML_2-1024×555.png 1024w, https://www.kdnuggets.com/wp-content/uploads/Rosidi_careers_in_AI_and_ML_2-768×416.png 768w, https://www.kdnuggets.com/wp-content/uploads/Rosidi_careers_in_AI_and_ML_2-1536×832.png 1536w, https://www.kdnuggets.com/wp-content/uploads/Rosidi_careers_in_AI_and_ML_2-2048×1110.png 2048w” sizes=”(max-width: 5818px) 100vw, 5818px”/>

ML is a subfield of ai that uses algorithms to build and deploy models that can learn from data and make decisions without the need for explicit programming instructions.

ai and ML Jobs

The complexity of ai and ML and their diverse purposes result in different jobs applying them differently.

Here are the ten jobs I'll be talking about.

<img decoding="async" class="aligncenter size-full wp-image-178528" alt="ai and ML Jobs ” width=”9043″ height=”2028″ srcset=”https://technicalterrence.com/wp-content/uploads/2024/08/1723723295_726_Beginner39s-Guide-to-Careers-in-AI-and-Machine-Learning.png 9043w, https://www.kdnuggets.com/wp-content/uploads/Rosidi_careers_in_AI_and_ML_3-300×67.png 300w, https://www.kdnuggets.com/wp-content/uploads/Rosidi_careers_in_AI_and_ML_3-1024×230.png 1024w, https://www.kdnuggets.com/wp-content/uploads/Rosidi_careers_in_AI_and_ML_3-768×172.png 768w, https://www.kdnuggets.com/wp-content/uploads/Rosidi_careers_in_AI_and_ML_3-1536×344.png 1536w, https://www.kdnuggets.com/wp-content/uploads/Rosidi_careers_in_AI_and_ML_3-2048×459.png 2048w” data-lazy-sizes=”(max-width: 9043px) 100vw, 9043px” src=”https://technicalterrence.com/wp-content/uploads/2024/08/1723723295_726_Beginner39s-Guide-to-Careers-in-AI-and-Machine-Learning.png”/><img decoding="async" class="aligncenter size-full wp-image-178528" src="https://technicalterrence.com/wp-content/uploads/2024/08/1723723295_726_Beginner39s-Guide-to-Careers-in-AI-and-Machine-Learning.png" alt="ai and ML Jobs ” width=”9043″ height=”2028″ srcset=”https://technicalterrence.com/wp-content/uploads/2024/08/1723723295_726_Beginner39s-Guide-to-Careers-in-AI-and-Machine-Learning.png 9043w, https://www.kdnuggets.com/wp-content/uploads/Rosidi_careers_in_AI_and_ML_3-300×67.png 300w, https://www.kdnuggets.com/wp-content/uploads/Rosidi_careers_in_AI_and_ML_3-1024×230.png 1024w, https://www.kdnuggets.com/wp-content/uploads/Rosidi_careers_in_AI_and_ML_3-768×172.png 768w, https://www.kdnuggets.com/wp-content/uploads/Rosidi_careers_in_AI_and_ML_3-1536×344.png 1536w, https://www.kdnuggets.com/wp-content/uploads/Rosidi_careers_in_AI_and_ML_3-2048×459.png 2048w” sizes=”(max-width: 9043px) 100vw, 9043px”/>

While all require ai and ML, and sometimes the skills and tools overlap, each job requires some distinct aspect of ai and ML expertise.

Here is an overview of these differences.

<img decoding="async" class="aligncenter size-full wp-image-178539" alt="ai and ML Jobs” width=”5906″ height=”5104″ srcset=”https://technicalterrence.com/wp-content/uploads/2024/08/1723723295_757_Beginner39s-Guide-to-Careers-in-AI-and-Machine-Learning.png 5906w, https://www.kdnuggets.com/wp-content/uploads/Rosidi_careers_in_ai_and_ml_4-300×259.png 300w, https://www.kdnuggets.com/wp-content/uploads/Rosidi_careers_in_ai_and_ml_4-1024×885.png 1024w, https://www.kdnuggets.com/wp-content/uploads/Rosidi_careers_in_ai_and_ml_4-768×664.png 768w, https://www.kdnuggets.com/wp-content/uploads/Rosidi_careers_in_ai_and_ml_4-1536×1327.png 1536w, https://www.kdnuggets.com/wp-content/uploads/Rosidi_careers_in_ai_and_ml_4-2048×1770.png 2048w” data-lazy-sizes=”(max-width: 5906px) 100vw, 5906px” src=”https://technicalterrence.com/wp-content/uploads/2024/08/1723723295_757_Beginner39s-Guide-to-Careers-in-AI-and-Machine-Learning.png”/><img decoding="async" class="aligncenter size-full wp-image-178539" src="https://technicalterrence.com/wp-content/uploads/2024/08/1723723295_757_Beginner39s-Guide-to-Careers-in-AI-and-Machine-Learning.png" alt="ai and ML Jobs” width=”5906″ height=”5104″ srcset=”https://technicalterrence.com/wp-content/uploads/2024/08/1723723295_757_Beginner39s-Guide-to-Careers-in-AI-and-Machine-Learning.png 5906w, https://www.kdnuggets.com/wp-content/uploads/Rosidi_careers_in_ai_and_ml_4-300×259.png 300w, https://www.kdnuggets.com/wp-content/uploads/Rosidi_careers_in_ai_and_ml_4-1024×885.png 1024w, https://www.kdnuggets.com/wp-content/uploads/Rosidi_careers_in_ai_and_ml_4-768×664.png 768w, https://www.kdnuggets.com/wp-content/uploads/Rosidi_careers_in_ai_and_ml_4-1536×1327.png 1536w, https://www.kdnuggets.com/wp-content/uploads/Rosidi_careers_in_ai_and_ml_4-2048×1770.png 2048w” sizes=”(max-width: 5906px) 100vw, 5906px”/>

1. ai Engineer

This role specializes in developing, deploying, testing, and maintaining ai systems.

Technical skills

The core skills of an ai engineer revolve around building ai models, so programming languages ​​and ML techniques are essential.

Tools

The main tools used are Python libraries, big data tools and databases.

• Tensor flow, PyTorch – creation of neural networks and ML applications using dynamic charts and static graph calculations
• Hadoop, Spark – processing and analysis big data
• scikit learning, Hard – implement supervised and unsupervised machine learning algorithms and construction models including DL Models
• SQL (for example, PostgreSQL, MySQL, SQL Server, Oracle), NoSQL databases such as MongoDB (for document-oriented datae.g, JSON-such as documents) and Cassandra (column family data model Great for time series data): Storing and managing structured and unstructured data

Projects

ai engineers work on automation projects and ai systems such as:

• Autonomous vehicles
• Virtual Assistants
• Healthcare robots
• Production line robots
• Smart home systems

Types of interview questions

Interview questions reflect the skills required, so expect the following topics:

2. Machine Learning Engineer

ML engineers develop, deploy, and maintain ML models. Their focus is Deploying and Model tuning in production.

Technical skills

The main skills of ML engineers, aside from the usual suspect in machine learning, are software engineering and advanced mathematics.

Tools

The tools of ML engineers are similar to those of ai engineers.

Projects

The knowledge of ML engineers is used in these projects:

Types of interview questions

ML is the core aspect of every ML engineer job, so this is the focus of their interviews.

• ML Concepts: Fundamentals of ML, e.g. types of machine learning, amazon.com/what-is/overfitting/” target=”_blank” rel=”nofollow noopener”>overfittingand lack of adaptation
• ML Algorithms
• Coding Questions
• Data Management: Fundamentals of Data Preparation for Modeling
• Model evaluation – Model evaluation techniques and metricsincluding accuracy, precision, recall, F1 score and ROC curve
• Questions to solve problems

3. Data scientist

Data scientists collect and clean data and perform exploratory data analysis (EDA) to better understand it. They create statistical models, ai+and+ml” target=”_blank” rel=”nofollow noopener”>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 focus on providing useful insights.

Tools

• Chart, Power BI – data visualization
• Tensor flow, scikit learning, Hard, PyTorch – development, training and implementation of ML and DL models
• Jupyter Notebooks – interactive coding, data visualization, documentation
• SQL and NoSQL databases: the same as the ML engineer
• Hadoop, Spark – same as ML engineer
• Pandas, NumPy, Science fiction – data manipulation and numerical calculations

Projects

Data scientists work on the same projects as ML engineers, just in the pre-implementation 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 ML fundamentals. In addition, they sometimes integrate ML algorithms into data pipelines, for example for data classification or anomaly detection.

Technical skills

• Programming languages ​​(Piton, Scale, Java, Tried) – data manipulation, big data processing, scripting, automation, creation data pipelinesManaging system processes and files
• Data storage – integrated data storage
• ETL (Extract, Transform, Load) processes – ETL pipeline construction
• Big data technologies – distributed storage, amazon.com/what-is/streaming-data/” target=”_blank” rel=”nofollow noopener”>data transmissionadvanced analysis
• Database management: data storage, security and availability
• ML: For ML-powered data pipelines

Tools

Projects

Data engineers work on projects that make data available to other roles.

• Building ETL pipelines
• Construction of systems for data transmission
• Assistance in the implementation of ML models

Types of interview questions

Data engineers must demonstrate knowledge of data architecture and infrastructure.

5. ai Research Scientist

These scientists conduct research focused on developing new ai algorithms and principles.

Technical skills

• Programming languages ​​(Piton, R) – data analysis, ai/glossary/data-science/model-prototyping/” target=”_blank” rel=”nofollow noopener”>Prototyping and the implementation of ai models
• Research methodology – design of experimentshypothesis formulation and testing, analysis of results
• Advanced ML: developing and refining algorithms
• NLP: Improving the capabilities of NLP systems
• DL – Improving the capabilities of DL systems

Tools

• Tensor flow, PyTorch – develop, train and deploy ML and 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

Scientists researching ai must demonstrate practical knowledge. and Very solid theoretical knowledge of 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

Business intelligence analysts analyze data, reveal useful insights, and present them to stakeholders using data visualizations, reports, and dashboards. ai in business intelligence is most often used to automate data processing, identify trends and patterns in data, and perform predictive analysis.

Technical skills

• Programming languages ​​(Piton) – query, processing, analysis, reporting and visualization of data
• Data analysis: providing useful information for decision making
• ai+and+ml” target=”_blank” rel=”nofollow noopener”>Business Analytics – identify opportunities and optimize business processes
• Data visualization: presenting information visually
• Machine learning: predictive analytics, anomaly detection, enhanced data insights

Tools

Projects

The projects they work on focus on analysis and reporting:

• Abandonment analysis
• Sales analysis
• Cost analysis
• Customer segmentation
• Process improvement, for example, inventory management.

Types of interview questions

BI analyst interview questions focus on coding and data analysis skills.

• Coding Questions
• Data and Database Fundamentals
• Fundamentals of data analysis
• Questions to solve problems

Conclusion

ai and ML are very broad and constantly evolving fields. As they evolve, so do the jobs that require ai and ML skills. New job descriptions and specializations appear almost every day, reflecting the growing need for companies to take advantage of the possibilities of ai and ML.

I have mentioned six jobs that I think you will be most interested in. However, these are not the only ai and ML jobs. There are many more and they will continue to appear, so try to keep up to date.

twitter.com/StrataScratch” rel=”noopener”>twitter.com/StrataScratch” target=”_blank” rel=”noopener noreferrer”>Nate Rosidi Nate is a data scientist and product strategy specialist. He is also an adjunct professor of analytics and is 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 trends in the job market, provides interview tips, shares data science projects, and covers all things SQL.