7 Artificial Intelligence (AI) Jobs You Can Pursue in 2024

Jobs in AI are exploding. But with all the hype, you might wonder - what exactly are the AI careers available, and how do you land one?

This article goes beyond simply listing entry-level AI jobs or senior positions. We'll delve into what it takes to be a leader in this field, whether you're a data science novice eager to embark on your AI career path or a seasoned professional looking to expand your skill set.

The 2024 AI landscape demands deep specialization and innovative thinking. AI advancements are impacting various industries, leading to new roles that require a blend of technical skills, ethical considerations, and creative problem-solving. Understanding these emerging opportunities is crucial for anyone interested in how to get a job in AI.

This guide equips you with the knowledge and strategic foresight needed to navigate the exciting world of AI careers. If you also want to know how to prepare for an AI career, check out our guide on how to learn AI from scratch in 2024. You can also learn about the top AI certifications for 2024.

The Growing Importance of AI in Various Industries

Contrary to popular belief, knowledge of AI and ML won't be restricted to just software development and work on large learning models (LLMs). Instead, various industries will see an influx of job openings in the coming years - let's see what these industries are.

AI in healthcare

The healthcare sector demands a nuanced understanding of medical sciences and advanced data analytics for professionals aiming to integrate AI effectively.

Entry into this domain typically requires expertise in machine learning, natural language processing (NLP), image recognition technologies, and a solid grounding in biological sciences or healthcare practices.

AI is revolutionizing healthcare through predictive analytics for patient care management, enhancing diagnostic accuracy with image analysis, and accelerating drug discovery through complex simulations.

Future applications are poised to personalize medicine further, improve remote patient monitoring through wearable AI technologies, and enable smarter decision-making in clinical trials.

AI in finance

Professionals entering AI in finance need a robust foundation in quantitative analysis, machine learning, and knowledge of financial instruments and markets. Proficiency in programming languages like Python, coupled with an understanding of regulatory and ethical considerations, is essential.

AI's current applications in finance include fraud detection through pattern recognition, algorithmic trading using predictive analytics, and personalized banking services via chatbots.

The future of AI in finance looks towards more sophisticated risk management models, enhanced regulatory compliance through automated systems, and deeper personalization of financial advice using AI-driven insights.

If you want to learn more about this, check out the webinar Artificial Intelligence in Finance: An Introduction in Python and the tutorial AI in Finance: Revolutionizing the Future of Financial Management.

AI in technology

The technology sector requires a deep technical proficiency in computer science, software engineering, and specific AI disciplines such as machine learning, deep learning, and robotics.

Experience with cloud computing platforms, big data technologies, and IoT is becoming increasingly important. AI applications in technology range from developing autonomous vehicles and drones, which rely on AI for navigation and safety, to smart home devices that use AI to learn and adapt to users' preferences.

Looking forward, AI is expected to drive innovations in quantum computing, enhance cybersecurity through predictive threat analysis, and further blur the lines between virtual and physical worlds through advanced augmented reality (AR) and virtual reality (VR) applications.

If you're looking for a learning resource, this is how to become an AI engineer.

AI in manufacturing

Individuals aspiring to enter the AI domain within manufacturing need a strong grasp of industrial engineering, machine learning algorithms, and robotics, along with practical skills in data analytics and IoT device management.

The integration of AI in manufacturing currently focuses on predictive maintenance to prevent equipment failure, optimizing production processes through real-time analytics, and automating quality control with vision systems.

Future trends include the adoption of digital twins to simulate manufacturing processes, advanced robotics for more flexible and autonomous production lines, and AI-driven supply chain optimization to respond dynamically to market changes.

This is a good resource to understand more about how AI is used in manufacturing.

AI in retail

AI applications in retail require expertise in data science, consumer psychology, and e-commerce technologies. Skills in machine learning, NLP for customer service bots, and predictive analytics for inventory management are critical.

AI enhances customer experiences through personalized recommendations, optimizes inventory with demand forecasting, and streamlines operations with automated checkout systems.

The future of AI in retail promises more immersive shopping experiences through augmented reality (AR), improved sustainability in supply chains through data-driven insights, and further personalization of marketing strategies to individual consumer behavior patterns.

If this industry piques your interest, learn more about generative AI in eCommerce.

7 Top AI Job Roles

Few industries haven't yet been turned upside down by the rapid advancement of AI. Knowing the AI fundamentals will soon be critical for everyone.

For AI-specific job roles, the requirements will be much higher, often requiring specific and high-level knowledge of adjacent subjects. With that in mind, let's take a look at the seven best jobs in AI you can pursue in 2024.

1. AI engineer

Salary range: $114K - $212K/yr (Glassdoor)

AI engineers are the architects of AI systems. They design, implement, and maintain AI models and infrastructure, bridging the gap between theoretical data science and practical application.

Their work involves natural language processing, machine learning, neural networks, and cloud applications. This discipline's most important programming languages are Python, Java, R, and C++.

A blend of software development skills and an understanding of advanced AI algorithms is crucial for success in this role. However, many aspiring data professionals are interested in AI engineering due to its practical applications.

To expand, check out the 13 essential AI engineer skills you need to know and learn how to become an AI engineer.

2. Data scientist

Salary range: $118K - $206K (Glassdoor)

Data scientists are the investigators, turning raw data into insights and strategies. They use statistical analysis, machine learning, and data processing techniques to uncover patterns and predict trends.

Their role is critical in making data-driven decisions that guide business strategies. To that end, experience with programming languages like Python or R and a strong foundation in statistics and machine learning is a core requirement for this career path.

DataCamp's curriculum can help you build a career from scratch and become a data scientist in Python or a data scientist in R.

3. Machine learning engineer

Salary range: $126K - $221K (Glassdoor)

Machine learning engineers specialize in creating algorithms and predictive models that allow computers to learn without being explicitly programmed for specific tasks. This role is pivotal in transforming data science models into applicable AI solutions that can scale across different platforms and environments.

Successful candidates possess a deep understanding of machine learning theory and the practical aspects of deploying them at scale.

An aspiring ML engineer must have proficiency in programming languages such as Python or Java and experience using machine learning libraries like TensorFlow or PyTorch.

These are the top 12 machine learning engineer skills to start your career, and this is how to become a machine learning engineer.

4. AI Researcher

Salary range: $100K - $186K (Glassdoor)

AI researchers push the boundaries of what is possible with AI. They conduct studies and experiments to develop new algorithms, improve existing models, and solve complex problems.

To excel as an AI researcher, one typically needs an advanced degree, such as a PhD in Computer Science or a related field, coupled with a track record of research and publications in AI. This role also demands practical skills in programming and data analysis, using tools and languages such as Python, R, TensorFlow, and PyTorch.

If you want to get more advanced with Python, try this introduction to deep learning with PyTorch and this introduction to Tensorflow in Python.

5. Robotics engineer

Salary range: $109K - $200K (Glassdoor)

Robotics engineers design, test, and implement robotic systems that interact intelligently with their environment. They integrate principles from various branches of engineering, including mechanical, electrical, and computer science, to create robots that can perform tasks autonomously or assist humans.

This role often requires a blend of software development skills for programming robot behaviors and hardware engineering skills for designing physical systems.

Key areas of focus include AI-driven automation, human-robot interaction, improving machine perception, and the development of autonomous vehicles and drones.

6. AI product manager

Salary range: $138K - $238K (Glassdoor)

AI product managers oversee the development and strategy of AI products, from conception through launch and beyond. They work at the intersection of business, technology, and user experience, ensuring that AI solutions meet customer needs while aligning with business goals.

This role demands a unique blend of skills: a solid understanding of AI and machine learning lifecycles, proficiency in product management practices, and the ability to lead cross-functional teams.

AI Product Managers must also navigate ethical considerations and regulatory challenges associated with AI technologies.

7. Ethics in AI specialist

Salary range: $85K - $170K (Glassdoor)

Ethics in AI specialists focus on the moral implications of AI technologies, working to ensure that AI systems are developed and deployed responsibly. They address bias, fairness, transparency, and accountability issues, often developing guidelines and best practices for ethical AI use.

This role requires a deep understanding of AI technologies, ethical theories and strong communication skills to advocate for ethical practices within organizations and across the industry.

Specialists often collaborate with AI researchers, developers, and policymakers to influence the design and regulation of AI systems.

If you want to learn more, you can take this course on AI ethics or read this article on ethics in generative AI.

Required Skills and Qualifications for These Roles

The AI field is dynamic and demands a robust skill set. While formal education in computer science, engineering, mathematics, or a related field can provide a strong foundation for AI, it's not the only path to success. The rise of accessible online learning resources makes it possible to develop the necessary skills independently.

Online platforms like DataCamp offer data science courses and AI fundamentals, providing hands-on experience with real-world datasets and projects. Degrees and certifications from accredited institutions add theoretical depth and credibility to one's expertise.

AI researchers, however, typically need advanced degrees, such as a PhD, coupled with publications in peer-reviewed journals.