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The extensive development of artificial intelligence (AI) and machine learning (ML) forced the job market to adapt. The era of AI and ML generalists has ended, and we entered the era of specialists.
It can be difficult even for more experienced to find their way around it, let alone beginners.
That’s why I created this little guide to understanding different AI and ML jobs.
What Are AI & ML?
AI is a field of computer science that aims to create computer systems that show human-like intelligence.
ML is a subfield of AI that employs algorithms to build and deploy models that can learn from data and make decisions without explicit instructions being programmed.
Jobs in AI & ML
The complexity of AI & ML and their various purposes results in various jobs applying them differently.
Here are the ten jobs I’ll talk about.
Though they all require AI & ML, with skills and tools sometimes overlapping, each job requires some distinct aspect of AI & ML expertise.
Here’s an overview of these differences.
1. AI Engineer
This role specializes in developing, implementing, testing, and maintaining AI systems.
Technical Skills
The core AI engineer skills revolve around building AI models, so programming languages and ML techniques are essential.
- Programming languages (Python, Java, C++) – model prototyping and development, big data, computer vision, and real-time systems
- Robotics
- Machine learning – expertise in different types of ML algorithms
- Big data analysis – preparing data for ML models, feature engineering, pattern and trend analysis
- Data modeling and engineering – storing data and building data pipelines for models
Tools
The main tools used are Python libraries, tools for big data, and databases.
- TensorFlow, PyTorch – creating neural networks and ML applications using dynamic graphs and static graphs computations
- Hadoop, Spark – processing and analyzing big data
- scikit-learn, Keras – implementing supervised and unsupervised ML algorithms and building models, including DL models
- SQL (e.g., PostgreSQL, MySQL, SQL Server, Oracle), NoSQL databases like MongoDB (for document-oriented data, e.g., JSON-like documents) and Cassandra (column-family data model excellent for time-series data) – storing and managing structured & unstructured data
Projects
The 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
The interview questions reflect the skills required, so expect the following topics:
- Neural networks
- ML algorithms
- Data preprocessing & feature engineering
- Coding questions
- Problem-solving questions
2. ML Engineer
ML engineers develop, deploy, and maintain ML models. Their focus is deploying and tuning models in production.
Technical Skills
ML engineers' main skills, apart from the usual suspect in machine learning, are software engineering and advanced mathematics.
- Programming languages (Python, Java, Julia, C++) – implementing ML algorithms, model development and deployment, numerical and scientific computing
- Advanced mathematics & statistics – understanding data, building algorithms, ML model optimization and evaluation
- Data analysis – cleaning and preparing data for model training
- ML algorithms – same as AI engineer
- Software engineering – version control and testing frameworks
Tools
The tools ML engineers’ tools are similar tools to AI engineers’.
- TensorFlow, PyTorch – developing, training, and deploying ML & DL models
- pandas, NumPy – data manipulation and numerical computation
- Jupyter Notebooks – interactive coding, data visualization, documentation
- Hadoop, Spark – same as AI engineer
- Docker, Kubernetes – ML applications containerization and management
- SQL and NoSQL databases – same as AI engineer
Projects
ML engineers’ knowledge is employed in these projects:
- Predictive analytics – predicting sales, customer churn, cost forecasting, inventory management
- NLP projects – chatbots, sentiment analysis
- Recommendation systems – suggesting products, content, and services to customers based on their behavior and preferences
- Anomaly detection – fraud prevention, cybersecurity
Types of Interview Questions
ML is the core aspect of every ML engineer job, so this is the focus of their interviews.
- ML concepts – ML fundamentals, e.g., types of machine learning, overfitting, and underfitting
- ML algorithms
- Coding questions
- Data handling – fundamentals of preparing data for modeling
- Model evaluation – model evaluation techniques and metrics, including 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 algorithms, and visualizations to understand patterns within 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 data scientists use are focused on providing actionable insights.
- Programming languages (Python, R) – querying databases, managing data, statistical analysis, ML algorithms, data visualization
- Statistical analysis – understand data, hypothesis testing, discovering trends
- Data visualization – visualizing trends and insights
- Machine learning
- Data wrangling – preparing data for analysis by cleaning, formatting, and structuring it
Tools
- Tableau, Power BI – data visualization
- TensorFlow, scikit-learn, Keras, PyTorch – developing, training, deploying 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 computation
Projects
Data scientists work on the same projects as ML engineers, only in the pre-deployment stages.
Types of Interview Questions
- ML algorithms
- Statistics & mathematics – descriptive statistics, inferential statistics, probability, regression analysis, experimentation & A/B testing, time series analysis, calculus, linear algebra
- Data cleaning and preparation
- Data visualization
- Problem-solving questions
4. Data Engineer
They develop and maintain data processing systems and build data pipelines to ensure data availability. Machine learning is not their core work. However, they collaborate with ML engineers and data scientists to ensure data availability for ML models, so they must understand the ML fundamentals. Also, they sometimes integrate ML algorithms into data pipelines, e.g., for data classification or anomaly detection.
Technical Skills
- Programming languages (Python, Scala, Java, Bash) – data manipulation, big data processing, scripting, automation, building data pipelines, managing system processes and files
- Data warehousing – integrated data storage
- ETL (Extract, Transform, Load) processes – building ETL pipelines
- Big data technologies – distributed storage, data streaming, advanced analytics
- Database management – data storage, security, and availability
- ML – for ML-driven data pipelines
Tools
- Hadoop, Spark – big data distributed storage and processing
- SQL and NoSQL databases – same as data scientist
- Informatica, Talend – data integration, ETL, and data management
- AWS, Google Cloud Platform – cloud computing
Projects
Data engineers work on projects that make data available for other roles.
- Building ETL pipelines
- Building systems for data streaming
- Assistance in deploying ML models
Types of Interview Questions
Data engineers must demonstrate knowledge of data architecture and infrastructure.
- Data engineering fundamentals
- ETL pipelines
- Data architecture
- Data scalability
- Coding
- Problem-solving questions
5. AI Research Scientist
These scientists conduct research focusing on developing new algorithms and AI principles.
Technical Skills
- Programming languages (Python, R) – data analysis, prototyping & deploying AI models
- Research methodology – experiment design, hypothesis formulation and testing, result analysis
- Advanced ML – developing and perfecting algorithms
- NLP – improving capabilities of NLP systems
- DL – improving capabilities of DL systems
Tools
- TensorFlow, PyTorch – developing, training, and deploying ML & DL models
- Jupyter Notebooks – interactive coding, data visualization, and documenting research workflows
- LaTeX – scientific writing
Projects
They work on creating and advancing algorithms used in:
- Intelligent automation
- Brain-computer interfaces
- Computational biology
- Safety in AI
- Other applications of AI & ML
Types of Interview Questions
The AI research scientists must show practical and very strong theoretical AI & ML knowledge.
- Theoretical foundations of AI & ML
- Practical application of AI
- ML algorithms – theory and application of different ML algorithms
- Methodology foundations
6. Business Intelligence Analyst
BI analysts analyze data, unveil actionable insights, and present them to stakeholders via data visualizations, reports, and dashboards. AI in business intelligence is most commonly used to automate data processing, identify trends and patterns in data, and predictive analytics.
Technical Skills
- Programming languages (Python) – data querying, processing, analysis, reporting, visualization
- Data analysis – providing actionable insights for decision making
- Business analytics – identifying opportunities and optimizing business processes
- Data visualization – presenting insights visually
- Machine learning – predictive analytics, anomaly detection, enhanced data insights
Tools
- Tableau, Power BI – data visualization, reporting, creating interactive dashboards
- Excel – data manipulation, analysis, and visualization
- SQL and NoSQL databases – same as data engineer
- SAS, SPSS – statistical analysis and predictive modeling
Projects
The projects they work on are focused on analysis and reporting:
- Churn analysis
- Sales analysis
- Cost analysis
- Customer segmentation
- Process improvement, e.g., inventory management
Types of Interview Questions
BI analysts' interview questions focus on coding and data analysis skills.
- Coding questions
- Data and database fundamentals
- Data analysis fundamentals
- Problem-solving questions
Conclusion
AI & ML are extensive and constantly evolving fields. As they evolve, the jobs that require AI & ML skills do, too. Almost every day, there are new job descriptions and specializations, reflecting the growing need for businesses to harness the possibilities of AI and ML.
I discussed six jobs I assessed you’ll be most interested in. However, these are not the only AI and ML jobs. There are many more, and they’ll keep coming, so try to stay up to date.
Nate Rosidi is a data scientist and in product strategy. He's also an adjunct professor teaching analytics, and is the founder of StrataScratch, a platform helping data scientists prepare for their interviews with real interview questions from top companies. Nate writes on the latest trends in the career market, gives interview advice, shares data science projects, and covers everything SQL.
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