8 answers
sara
Student
Tehran, Tehran Province, Iran
1
Question
Asked
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Is using data for artificial intelligence automation the job of an artificial intelligence engineer?
How is a working day of an artificial intelligence engineer?
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8 answers
Madi Martinez
Product Manager, Project Manager, Customer Success
8
Answers
Charlotte, North Carolina
Updated
Madi’s Answer
Hello Sara,
The majority of technical tasks in the field of AI and Generative AI are typically handled by Data Scientists and developers. However, this doesn't mean that there aren't other ways to contribute to this field without directly engaging with the data. Many organizations actively seek a diverse team beyond just Data Scientists, to help maintain the product's progression, interact with clients to promote their work, and perform other essential tasks. Most engineers appreciate having the time to focus on their work and they often find the presence of project managers beneficial in managing distractions and unnecessary interruptions.
The majority of technical tasks in the field of AI and Generative AI are typically handled by Data Scientists and developers. However, this doesn't mean that there aren't other ways to contribute to this field without directly engaging with the data. Many organizations actively seek a diverse team beyond just Data Scientists, to help maintain the product's progression, interact with clients to promote their work, and perform other essential tasks. Most engineers appreciate having the time to focus on their work and they often find the presence of project managers beneficial in managing distractions and unnecessary interruptions.
Patrick Meyer
Executive - Data Analytics and AI
724
Answers
Naperville, Illinois
Updated
Patrick’s Answer
Sara, I want to first thank you for reaching out and asking your questions regarding the life of a AI engineer. Sara, you should know that exploring the realm of using data for artificial intelligence automation is indeed a significant aspect of the job of an artificial intelligence (AI) engineer. I hope the information that I have below provides you some insight and/or help.
As an AI engineer, your main task would be to craft and refine algorithms that empower machines to learn from data, make smart choices, and automate intricate tasks. The process of working with data encompasses activities like data preprocessing, feature engineering, and model training. Here, you'll utilize machine learning techniques to unearth significant patterns.
The nature of your workday as an AI engineer can vary greatly depending on the organization or environment you're part of. It could involve team collaborations, staying abreast of the latest advancements through research, and fine-tuning existing models to improve their efficiency.
Moreover, AI engineers frequently invest time in coding, testing, and improving algorithms to boost their performance. It's important, Sara, to remember that this field thrives on continuous learning, given the ever-changing landscape of AI. The job provides a stimulating setting where problem-solving skills and creativity are highly prized, ensuring each day is unique and mentally rewarding. I urge you, Sara, to dive into this thrilling field by getting practical experience with AI projects and maintaining a keen interest in the newest developments in artificial intelligence.
As an AI engineer, your main task would be to craft and refine algorithms that empower machines to learn from data, make smart choices, and automate intricate tasks. The process of working with data encompasses activities like data preprocessing, feature engineering, and model training. Here, you'll utilize machine learning techniques to unearth significant patterns.
The nature of your workday as an AI engineer can vary greatly depending on the organization or environment you're part of. It could involve team collaborations, staying abreast of the latest advancements through research, and fine-tuning existing models to improve their efficiency.
Moreover, AI engineers frequently invest time in coding, testing, and improving algorithms to boost their performance. It's important, Sara, to remember that this field thrives on continuous learning, given the ever-changing landscape of AI. The job provides a stimulating setting where problem-solving skills and creativity are highly prized, ensuring each day is unique and mentally rewarding. I urge you, Sara, to dive into this thrilling field by getting practical experience with AI projects and maintaining a keen interest in the newest developments in artificial intelligence.
Mohitha Chandrashekara
Solution Delivery Manager
2
Answers
Dublin, California
Updated
Mohitha’s Answer
Absolutely! As an artificial intelligence engineer, you'll be at the heart of AI automation, using data to breathe life into machines. Your role is to create and apply algorithms and models that empower machines to learn from and make decisions based on data. This exciting journey includes steps like gathering data, prepping it, crafting features, training models, and assessing their performance. Plus, you'll be the guardian of data quality, making sure it's top-notch and accurately reflects the problem you're solving. It's a role filled with opportunities to innovate and make a real impact!
David Vickers
Architect
2
Answers
Agawam, Massachusetts
Updated
David’s Answer
Hello Sara,
In terms of utilizing data for artificial intelligence automation, traditionally, the bulk of AI work has been under the domain of Data Science. If you previously if you had a keen interest in AI, your path would have likely led you to becoming a data scientist. However, the landscape is changing rapidly. We're seeing an increasing number of roles in the field that apply the outcomes of data science team's efforts. This is a relatively new area, so job descriptions for positions titled 'Artificial Intelligence Engineer' can vary significantly as the industry is still defining the precise nature of this role.
Here's a typical job description for your reference:
AI engineers are chiefly tasked with using diverse programming algorithms to construct, test, and implement AI models. Their other responsibilities encompass:
- Collaborating with other team members
- Establishing and overseeing the AI development process and the product's overall infrastructure
- Conducting statistical analysis and using the results to guide the organization's decision-making process
- Automating crucial infrastructure for the data science team
- Developing infrastructures for data transformation and ingestion
- Constructing AI models that make predictions based on vast amounts of data
- Communicating the value of the AI models they create to a broad audience within the organization, including team members and product managers
- Converting the machine learning models into APIs for interaction with other applications.
In terms of utilizing data for artificial intelligence automation, traditionally, the bulk of AI work has been under the domain of Data Science. If you previously if you had a keen interest in AI, your path would have likely led you to becoming a data scientist. However, the landscape is changing rapidly. We're seeing an increasing number of roles in the field that apply the outcomes of data science team's efforts. This is a relatively new area, so job descriptions for positions titled 'Artificial Intelligence Engineer' can vary significantly as the industry is still defining the precise nature of this role.
Here's a typical job description for your reference:
AI engineers are chiefly tasked with using diverse programming algorithms to construct, test, and implement AI models. Their other responsibilities encompass:
- Collaborating with other team members
- Establishing and overseeing the AI development process and the product's overall infrastructure
- Conducting statistical analysis and using the results to guide the organization's decision-making process
- Automating crucial infrastructure for the data science team
- Developing infrastructures for data transformation and ingestion
- Constructing AI models that make predictions based on vast amounts of data
- Communicating the value of the AI models they create to a broad audience within the organization, including team members and product managers
- Converting the machine learning models into APIs for interaction with other applications.
Sachin Joshi
Software engineer
23
Answers
San Jose, California
Updated
Sachin’s Answer
A typical working day for an artificial intelligence (AI) engineer can vary depending on their specific role, responsibilities, and the organization they work for. However, here's a general overview of what a day might look like for an AI engineer:
Morning Routine:
Check emails and messages for any urgent communications.
Review the agenda for the day and prioritize tasks.
Research and Development:
Work on developing and improving AI models and algorithms.
Collaborate with team members to brainstorm ideas and problem-solve.
Stay updated on the latest research papers and advancements in the field.
Coding and Programming:
Write and debug code for AI applications.
Implement machine learning algorithms and models using programming languages like Python, Java, or C++.
Data Analysis:
Analyze and preprocess large datasets for training and testing AI models.
Ensure data quality and make adjustments as needed.
Collaboration:
Attend meetings with cross-functional teams to discuss project progress, challenges, and potential solutions.
Collaborate with data scientists, software engineers, and other stakeholders.
Testing and Evaluation:
Conduct experiments to evaluate the performance of AI models.
Debug and optimize models based on testing results.
Documentation:
Document code, algorithms, and project-related information.
Create documentation for users and team members.
Meetings and Communication:
Participate in team meetings, scrums, and discussions.
Communicate progress, challenges, and ideas with team members and project stakeholders.
Continuous Learning:
Stay informed about advancements in AI and related technologies.
Engage in professional development activities such as online courses, workshops, or conferences.
Project Management:
Manage project timelines and milestones.
Coordinate with project managers to ensure alignment with overall project goals.
Problem Solving:
Address and resolve challenges related to model performance, data quality, or other technical issues.
Flexible Schedule:
AI engineers often have a flexible schedule, allowing for focused work sessions as well as collaboration time.
It's important to note that the specific tasks and the balance between them can vary based on the organization, the stage of the project, and the engineer's role within the team. Additionally, AI engineers may work on tasks related to natural language processing, computer vision, robotics, or other AI subfields, depending on their expertise and project requirements.
Morning Routine:
Check emails and messages for any urgent communications.
Review the agenda for the day and prioritize tasks.
Research and Development:
Work on developing and improving AI models and algorithms.
Collaborate with team members to brainstorm ideas and problem-solve.
Stay updated on the latest research papers and advancements in the field.
Coding and Programming:
Write and debug code for AI applications.
Implement machine learning algorithms and models using programming languages like Python, Java, or C++.
Data Analysis:
Analyze and preprocess large datasets for training and testing AI models.
Ensure data quality and make adjustments as needed.
Collaboration:
Attend meetings with cross-functional teams to discuss project progress, challenges, and potential solutions.
Collaborate with data scientists, software engineers, and other stakeholders.
Testing and Evaluation:
Conduct experiments to evaluate the performance of AI models.
Debug and optimize models based on testing results.
Documentation:
Document code, algorithms, and project-related information.
Create documentation for users and team members.
Meetings and Communication:
Participate in team meetings, scrums, and discussions.
Communicate progress, challenges, and ideas with team members and project stakeholders.
Continuous Learning:
Stay informed about advancements in AI and related technologies.
Engage in professional development activities such as online courses, workshops, or conferences.
Project Management:
Manage project timelines and milestones.
Coordinate with project managers to ensure alignment with overall project goals.
Problem Solving:
Address and resolve challenges related to model performance, data quality, or other technical issues.
Flexible Schedule:
AI engineers often have a flexible schedule, allowing for focused work sessions as well as collaboration time.
It's important to note that the specific tasks and the balance between them can vary based on the organization, the stage of the project, and the engineer's role within the team. Additionally, AI engineers may work on tasks related to natural language processing, computer vision, robotics, or other AI subfields, depending on their expertise and project requirements.
Sahida Khatun
Software Developer
68
Answers
Kolkata, West Bengal, India
Updated
Sahida’s Answer
Hi Sara,
Yes, utilizing data for artificial intelligence (AI) and automation is a crucial aspect of the job of an artificial intelligence engineer. AI engineers are responsible for designing, developing, and implementing AI models and systems that can analyze and interpret data, make decisions, and perform tasks without explicit programming.
The working day of an AI engineer can vary depending on the specific role, industry, and project requirements. However, here are some common activities that an AI engineer may engage in during a typical day:
Problem Definition: Understand the problem or task that the AI system needs to address. This involves discussions with stakeholders to gather requirements and expectations.
Data Collection and Preprocessing: Collect relevant data for training and testing the AI model. This may involve cleaning and preprocessing the data to ensure it is suitable for the chosen machine learning algorithms.
Algorithm Selection and Model Development: Choose appropriate algorithms and methods based on the problem at hand. Develop, train, and test machine learning models using the selected data.
Programming and Coding: Write code to implement AI models. This may involve using programming languages like Python, along with libraries and frameworks like TensorFlow or PyTorch.
Testing and Validation: Evaluate the performance of the developed models using validation and test datasets. Fine-tune the models to achieve desired outcomes.
Deployment: Once a model is ready, deploy it in the intended environment. This may involve integration with existing systems and ensuring that the AI model works seamlessly in a production environment.
Monitoring and Maintenance: Continuously monitor the performance of deployed models and make necessary updates or improvements. Address any issues that may arise during real-world usage.
Collaboration and Communication: Work closely with cross-functional teams, including data scientists, software developers, and domain experts. Effective communication is essential for understanding requirements and conveying technical concepts to non-technical stakeholders.
Research and Learning: Stay updated on the latest developments in AI and machine learning. Engage in ongoing learning to incorporate new techniques and best practices into projects.
Documentation: Document the entire process, including the problem definition, data sources, model architecture, and implementation details. This documentation is important for knowledge transfer and future reference.
It's important to note that the field of AI is dynamic, and the tasks and responsibilities of AI engineers may evolve as new technologies and methodologies emerge. Additionally, the specific responsibilities can vary depending on the organization and the stage of the AI development lifecycle.
Kind Regards,
Sahida
Yes, utilizing data for artificial intelligence (AI) and automation is a crucial aspect of the job of an artificial intelligence engineer. AI engineers are responsible for designing, developing, and implementing AI models and systems that can analyze and interpret data, make decisions, and perform tasks without explicit programming.
The working day of an AI engineer can vary depending on the specific role, industry, and project requirements. However, here are some common activities that an AI engineer may engage in during a typical day:
Problem Definition: Understand the problem or task that the AI system needs to address. This involves discussions with stakeholders to gather requirements and expectations.
Data Collection and Preprocessing: Collect relevant data for training and testing the AI model. This may involve cleaning and preprocessing the data to ensure it is suitable for the chosen machine learning algorithms.
Algorithm Selection and Model Development: Choose appropriate algorithms and methods based on the problem at hand. Develop, train, and test machine learning models using the selected data.
Programming and Coding: Write code to implement AI models. This may involve using programming languages like Python, along with libraries and frameworks like TensorFlow or PyTorch.
Testing and Validation: Evaluate the performance of the developed models using validation and test datasets. Fine-tune the models to achieve desired outcomes.
Deployment: Once a model is ready, deploy it in the intended environment. This may involve integration with existing systems and ensuring that the AI model works seamlessly in a production environment.
Monitoring and Maintenance: Continuously monitor the performance of deployed models and make necessary updates or improvements. Address any issues that may arise during real-world usage.
Collaboration and Communication: Work closely with cross-functional teams, including data scientists, software developers, and domain experts. Effective communication is essential for understanding requirements and conveying technical concepts to non-technical stakeholders.
Research and Learning: Stay updated on the latest developments in AI and machine learning. Engage in ongoing learning to incorporate new techniques and best practices into projects.
Documentation: Document the entire process, including the problem definition, data sources, model architecture, and implementation details. This documentation is important for knowledge transfer and future reference.
It's important to note that the field of AI is dynamic, and the tasks and responsibilities of AI engineers may evolve as new technologies and methodologies emerge. Additionally, the specific responsibilities can vary depending on the organization and the stage of the AI development lifecycle.
Kind Regards,
Sahida
Gustavo Calzada
Solution Architect
2
Answers
Guadalajara
Updated
Gustavo’s Answer
Hi Sara!
As you may know, data is one of the foundations for most applications nowadays. AI is also one of the areas that require a huge amount of data. However, usually not only the quantity of data but the quality is key to have successful results. This means that AI processes require that the data we ingest is properly assessed and "cleaned up" as much as possible. Poor data quality often leads to poor outcomes. Therefore Data Scientists, Data Engineers and AI Engineers have a great focus on what data is being fed to the AI algorithms.
From Data Science's perspective, we usually think of the general process as Cooking. In this sense, Engineers would be the chefs and Data would be the ingredients. The better the ingredients, the better the outcome.
Having this in mind, most of the AI Engineers and Data Scientists I work with usually would have different kind of days depending on their specific role, scope, type of project, phase they are working on and more. However, in general we can agree that Communication and collaboration with other team members is fundamental, so they talk quite often. This is very important because they need to understand the problem and make an abstraction of it. Other regular activity to assess the quality of the data they are provided with. They need to ensure that they have the Ingredients (data), the tools, the environment and the people necessary to provide the best outcome they can come up with. Another key activity is to define, to develop, to assess and to review the algorithms and models they require for their project (you can related it as the cooking techniques they require to complete the dish they are creating). And lastly but not less important is to report and document everything that was required for the project, also the findings, blockers, achievements, concerns, constrains they are engaging. Going back to the beginning, communication is very important.
But far more important, AI Engineers have fun! I like to see them also as explorers. Leaders in pursuit of the discovery of new horizons concealed within the dept of, sometimes, the unknown.
I hope this may help you picture a bit of their most common activities.
As you may know, data is one of the foundations for most applications nowadays. AI is also one of the areas that require a huge amount of data. However, usually not only the quantity of data but the quality is key to have successful results. This means that AI processes require that the data we ingest is properly assessed and "cleaned up" as much as possible. Poor data quality often leads to poor outcomes. Therefore Data Scientists, Data Engineers and AI Engineers have a great focus on what data is being fed to the AI algorithms.
From Data Science's perspective, we usually think of the general process as Cooking. In this sense, Engineers would be the chefs and Data would be the ingredients. The better the ingredients, the better the outcome.
Having this in mind, most of the AI Engineers and Data Scientists I work with usually would have different kind of days depending on their specific role, scope, type of project, phase they are working on and more. However, in general we can agree that Communication and collaboration with other team members is fundamental, so they talk quite often. This is very important because they need to understand the problem and make an abstraction of it. Other regular activity to assess the quality of the data they are provided with. They need to ensure that they have the Ingredients (data), the tools, the environment and the people necessary to provide the best outcome they can come up with. Another key activity is to define, to develop, to assess and to review the algorithms and models they require for their project (you can related it as the cooking techniques they require to complete the dish they are creating). And lastly but not less important is to report and document everything that was required for the project, also the findings, blockers, achievements, concerns, constrains they are engaging. Going back to the beginning, communication is very important.
But far more important, AI Engineers have fun! I like to see them also as explorers. Leaders in pursuit of the discovery of new horizons concealed within the dept of, sometimes, the unknown.
I hope this may help you picture a bit of their most common activities.
James Constantine Frangos
Consultant Dietitian & Software Developer since 1972 => Nutrition Education => Health & Longevity => Self-Actualization.
6075
Answers
Gold Coast, Queensland, Australia
Updated
James Constantine’s Answer
Dear Sara,
Does the role of an artificial intelligence engineer encompass the use of data for AI automation?
Absolutely, the utilization of data for AI automation is a pivotal part of an artificial intelligence engineer's role. These engineers are tasked with the creation, application, and upkeep of AI systems that can learn from data, make informed decisions, and execute tasks that typically need human intelligence. Data is the backbone in training AI models, allowing them to identify patterns, forecast outcomes, and consistently enhance their performance.
Artificial intelligence engineers handle large datasets to train machine learning models, which are the heart of AI automation. They clean and preprocess data, choose suitable algorithms, train models, assess their performance, and refine them to achieve the desired results. The collection, labeling, storage, processing, and analysis of data are all integral parts of the AI engineering process.
In a nutshell, the use of data for AI automation is a primary duty of artificial intelligence engineers. They are responsible for creating and developing intelligent systems that can automate tasks and make decisions based on data-driven insights.
What does a typical day look like for an artificial intelligence engineer?
The daily routine of an artificial intelligence engineer can differ based on their specific role, the industry they're in, and the project they're working on. However, there are some common tasks and activities that AI engineers usually undertake during their workday:
Research and Planning: AI engineers often kick off their day by exploring new algorithms, techniques, or tools that could enhance their AI projects. They also plan the tasks they need to complete during the day.
Data Preparation: A substantial part of an AI engineer's day is devoted to preparing and processing data for training machine learning models. This includes cleaning data, addressing missing values, scaling features, and other preprocessing tasks.
Model Development: AI engineers spend time designing, developing, and training machine learning models based on the project's needs. They experiment with different algorithms and hyperparameters to optimize model performance.
Testing and Evaluation: Testing the trained models on validation datasets and evaluating their performance metrics is another vital part of an AI engineer's daily routine. They analyze results, pinpoint areas for improvement, and iterate on the model development process.
Collaboration: AI engineers often work together with cross-functional teams such as data scientists, software developers, domain experts, and project managers to ensure that AI solutions are in line with business goals and requirements.
Documentation: Documenting the entire AI development process, including data sources, model architecture, training procedures, and results is essential for reproducibility and knowledge sharing within the team.
Continuous Learning: Keeping up-to-date with the latest trends in artificial intelligence, attending conferences or workshops, participating in online courses or reading research papers are all part of a proactive AI engineer's routine to enhance their skills and knowledge.
In summary, a typical day for an artificial intelligence engineer involves a blend of research, data preparation, model development, testing, collaboration with colleagues, documentation, and continuous learning to foster innovation in AI projects.
Top 3 Authoritative Sources Used:
IEEE Xplore
SpringerLink
arXiv
May God bless you!
James Constantine.
Does the role of an artificial intelligence engineer encompass the use of data for AI automation?
Absolutely, the utilization of data for AI automation is a pivotal part of an artificial intelligence engineer's role. These engineers are tasked with the creation, application, and upkeep of AI systems that can learn from data, make informed decisions, and execute tasks that typically need human intelligence. Data is the backbone in training AI models, allowing them to identify patterns, forecast outcomes, and consistently enhance their performance.
Artificial intelligence engineers handle large datasets to train machine learning models, which are the heart of AI automation. They clean and preprocess data, choose suitable algorithms, train models, assess their performance, and refine them to achieve the desired results. The collection, labeling, storage, processing, and analysis of data are all integral parts of the AI engineering process.
In a nutshell, the use of data for AI automation is a primary duty of artificial intelligence engineers. They are responsible for creating and developing intelligent systems that can automate tasks and make decisions based on data-driven insights.
What does a typical day look like for an artificial intelligence engineer?
The daily routine of an artificial intelligence engineer can differ based on their specific role, the industry they're in, and the project they're working on. However, there are some common tasks and activities that AI engineers usually undertake during their workday:
Research and Planning: AI engineers often kick off their day by exploring new algorithms, techniques, or tools that could enhance their AI projects. They also plan the tasks they need to complete during the day.
Data Preparation: A substantial part of an AI engineer's day is devoted to preparing and processing data for training machine learning models. This includes cleaning data, addressing missing values, scaling features, and other preprocessing tasks.
Model Development: AI engineers spend time designing, developing, and training machine learning models based on the project's needs. They experiment with different algorithms and hyperparameters to optimize model performance.
Testing and Evaluation: Testing the trained models on validation datasets and evaluating their performance metrics is another vital part of an AI engineer's daily routine. They analyze results, pinpoint areas for improvement, and iterate on the model development process.
Collaboration: AI engineers often work together with cross-functional teams such as data scientists, software developers, domain experts, and project managers to ensure that AI solutions are in line with business goals and requirements.
Documentation: Documenting the entire AI development process, including data sources, model architecture, training procedures, and results is essential for reproducibility and knowledge sharing within the team.
Continuous Learning: Keeping up-to-date with the latest trends in artificial intelligence, attending conferences or workshops, participating in online courses or reading research papers are all part of a proactive AI engineer's routine to enhance their skills and knowledge.
In summary, a typical day for an artificial intelligence engineer involves a blend of research, data preparation, model development, testing, collaboration with colleagues, documentation, and continuous learning to foster innovation in AI projects.
Top 3 Authoritative Sources Used:
IEEE Xplore
SpringerLink
arXiv
May God bless you!
James Constantine.