10 AI Job Roles which you can build a Career in

AI Product Manager

Product Management is the practice of developing, launching, and supporting products. It is often represented at the intersection of Business, Technology and User Experience (UX). An AI product manager needs to have a deep understanding of artificial intelligence and machine learning. They should know how the product works, what it does, how it improves a customer’s experience, and what role AI will play within the product.

The role of an AI product manager varies based on the organization. Below are the general responsibilities of a Product Manager:

1. Defining the product vision and strategy

2. Conducting market research

3. Defining the KPIs for measuring the Product Success

4. Prioritizing features (functional and non-functional requirements)

5. Overseeing the product development process from ideation to launch

6. Communicate the Product Features Status and Delivery Plan to all the Stakeholders

7. Measuring the Product Success based on the defined KPIs

AI Ethicist

An AI Ethicist analyze the potential consequences of AI, including environmental impact, technology misuse, value alignment, and transparency and helps to address the ethical questions and implications of using artificial intelligence within an organization. In this role, you typically work in a corporate responsibility department. AI ethicists consider the legal, social, and moral implications of AI and other information technologies and then prepare presentations or reports addressing those implications.

Below are the general responsibilities of an AI Ethicist:

1. Collaborating with all stakeholders to include moral boundaries in AI design

2. Creating compliance processes and internal guidance to mitigate harm

3. Establishing standards for ethical AI development

4. Educating and training others on ethical AI practices

5. Conducting reviews of AI systems for ethical issues before deployment

6. Participating in continuing education for current AI trends

AI Research Scientist

AI research scientists contribute in developing new algorithms, experimenting with machine learning models, and improving the functionality of AI systems. Thus, they would be focusing on the innovations in areas like generative AI, natural language processing (NLP), robotics, and computer vision.

AI Research Scientist should have the below skills.

1. Algorithm development

2. Big data technologies

3. Deep learning frameworks

4. Machine learning

5. Mathematical modeling

6. Natural language processing (NLP)

7. Statistical analysis

8. Programming

AI Engineer

AI engineers uses pre-trained models and existing AI tools to improve user experience.

They use AI and machine learning (ML) techniques to develop applications and systems to help organizations increase efficiency

AI Engineer must have the below skills to excel in the role:

1. Familiarity with generative AI frameworks and architectures

2. Experience with LLMs and their applications.

3. Knowledge of prompt engineering

4. Fine-tuning techniques for generative AI models.

5. Ability to program the system to process and analyze large data sets.

6. Good knowledge of Natural language processing

7. Proficiency in working with cloud-based AI platforms and services.

Business Intelligence (BI) Developer

A Business Intelligence (BI) developer helps the organizations to transform the data into clear, actionable insights. They consume the complex data and simplify it using specialized software and tools, enabling businesses to make informed decisions.

In the context of AI, BI developer supports the AI initiatives in the organizations by evaluating the quality of the data used.

Data Scientist

Data Scientist collects, and analyze large amount of data for business needs. They use analytical, statistical, and programing methods to transform the data to logical and precise business insights.

1. Start with the Data discovery

2. Collect the data

3. Process and Clean the data

4. Store the data

5. Do initial investigations on the data

6. Select the models and algorithms

7. Apply Machine Learning and Statistical Modeling

8. Measure the results

9. Present the results to the Stakeholders

10. Enhance the data based on the feedback

Machine Learning Engineer

A Machine Learning Engineer should skilled enough for designing and developing machine learning systems, implementing appropriate ML algorithms, and conducting experiments. These systems would leverage huge data sets to generate and develop algorithms capable of learning and eventually making predictions. So the Machine Learning Engineer should have strong programming skills, knowledge of data science, and expertise in statistics

1. Understand the business case for the AI System

2. Do research and select the appropriate data sets before performing data collection and data modeling

3. Learn, Transform and Convert data science prototypes

4. Analyze the use cases of ML algorithms

5. Design and develop the Machine Learning systems, models, and schemes

6. Perform statistical analysis and using results to improve models

7. Do the Training and retraining of the ML systems and models as needed

8. Enhance the existing ML frameworks and libraries

MLOps Engineer

An MLOps Engineer co-ordinates with the data scientist , AI Developers and IT Operations team and is responsible for deploying, monitoring, and maintaining machine learning models in production environments. So she should be having a mix of skills such as Machine Learning, Software Engineering and Operation.

Below are the core responsibilities of MLOps Engineer

1. Design, develop, and implement MLOps pipelines for the continuous deployment and integration of machine learning models.

2. Work with data scientists and engineers to understand model requirements and optimize deployment processes.

3. Optimize machine learning pipelines for scalability, efficiency and cost-effectiveness.

4. Troubleshoot and resolve issues related to model deployment and performance.

5. Implement best practices for version control, model reproducibility and governance.

6. Ensure compliance with security and data privacy standards in all MLOps activities.

7. Continuously monitor and maintain models in production, ensuring optimal performance, accuracy and reliability.

8. Do the Automation of the training, testing and deployment processes for machine learning models.

9. Keep up-to-date with the latest MLOps tools, technologies and trends.

UX Developer

UX developer working on any project utilizing the power of AI, should be able to make use of AI tools to predict the User behavior patterns. This would help to come up with the perfect and incredible User Experience on the Application which the end users are going to use. Below are the some of the tasks which the UX developer would be performing:

1. Use the AI based User Journey Predictions

2. Design by collaborating with the AI Assistants

3. Use AI Analytical Tools to get the User Feedback on User Experience

4. Create Personalized User experience using the analysis of Users data by AI

5. Enhance the Application Security with the support of AI

Vision Scientists/Engineers

Computer Vision Engineers bridge the gap between raw visual data and actionable insights. Thus, they specialize in researching algorithms and systems that enable machines to interpret and apply visual information from the world, such as images and videos. They possess impressive knowledge in topics such as machine learning, deep learning, image annotation, image and video segmentation, and image recognition, etc.

1. Work closely with other engineers to build hardware and software leveraging visual information to solve problems or perform specific tasks.

2. They create and update algorithms which enable machines to interpret visual data and make decisions.

3. They apply advanced techniques in image processing and deep learning to tasks such as image recognition, object detection, segmentation, and pattern recognition.

4. They would spend their time researching, training, testing, and deploying models that are implemented in computer vision applications to solve real-world problems.