Artificial intelligence (AI) is one of the most exciting and rapidly growing fields today. As AI continues to transform industries and impact our everyday lives, there is increasing demand for AI expertise across sectors.
According to a report by MarketsandMarkets, the global AI market is projected to grow from $27.23 billion in 2019 to $266.92 billion by 2027.
This meteoric rise is fueled by the adoption of AI solutions for things like predictive analytics, natural language processing, computer vision, and robotic process automation by organizations across finance, healthcare, retail, automotive, and more.
This creates many promising career opportunities for those interested in working with AI. As per LinkedIn’s 2020 Emerging Jobs report, machine learning engineer, data scientist, and AI specialist are some of the top emerging roles with high growth rates over the past five years. Jobs requiring AI skills also pay well with the median base salary for AI jobs ranging from $116,000 to $170,000 per year based on analysis by Indeed.
Here is an overview of some of the main career paths in artificial intelligence and how to break into the field so you can take advantage of the high demand for AI talent. The roles described range from being on the cutting edge of building new AI models as an AI researcher to integrating AI into business operations as an AI consultant or product manager. Read on to find which career aligns best with your interests and strengths.
AI Researcher
AI researchers invent new machine learning algorithms, advance techniques like neural networks and natural language processing, and find ways to apply AI to solve real-world problems. This role requires a strong foundation in subjects like computer science, mathematics, statistics, data science, and software engineering.
To become an AI researcher, you typically need a master’s or Ph.D. in computer science or a related technical field. During your studies, focus on classes in artificial intelligence, machine learning, data mining, algorithms, and math. Gain relevant experience through internships at technology companies or research positions at universities under professors conducting AI research.
As an AI researcher, you can work at technology companies like Google, Meta, Microsoft, etc. developing new AI models and systems. Or you can work at research labs and universities advancing the state-of-the-art in AI. The highest demand is for those with expertise in deep learning and machine learning.
Machine Learning Engineer
Machine learning engineers develop and optimize machine learning systems and models to solve problems. They are responsible for preparing data, training models, deploying models into production, monitoring performance, and iterating to improve accuracy.
To become a machine learning engineer, you need a bachelor’s degree in computer science, data science, math, or a related technical field. Learn skills like Python, data analysis, machine learning frameworks (TensorFlow, PyTorch), cloud platforms (AWS, GCP), and software engineering. Complete internships or projects using machine learning for real-world applications.
Machine learning engineers can work across many industries developing AI systems for things like self-driving cars, speech recognition, predictive analytics, computer vision, and more. Top companies hiring machine learning engineers include Google, Amazon, Microsoft, Apple, Uber, and many startups. The work blends both software engineering and statistical modeling.
Data Scientist
Data scientists utilize their analytical and coding skills to extract insights from complex data sets. They implement machine learning algorithms and statistical models to solve problems in fields like finance, healthcare, marketing, etc.
To become a data scientist, you typically need a master’s degree in data science, computer science, statistics or a quantitative field. Take courses in statistical modeling, data mining, programming, and databases. Build up your data analysis skills using Python, R, SQL, Hadoop, Spark, etc. Complete an internship or analyse real-world data sets through projects.
Data scientists are in high demand across almost all industries. You can work for tech firms like Facebook, LinkedIn, Airbnb, etc. to improve products and services. Or work for traditional companies like retail, insurance, healthcare to guide business decisions through data insights. Strong math skills and creativity are essential to succeed as a data scientist.
Business Intelligence (BI) Developer
BI developers design and implement data systems that help businesses make strategic decisions based on data analytics. They build data warehouses, create ETL pipelines, visualize data through reports and dashboards, and develop BI solutions.
To get started in BI development, you need a bachelor’s degree in information systems, computer science or a related quantitative field. Learn technical skills like SQL, ETL tools, reporting, data modeling, and data visualization. Familiarize yourself with BI infrastructure and platforms like Teradata, Oracle, SAP, Microsoft Power BI, and Tableau. Complete internships or projects to gain hands-on experience.
BI developers are employed by large corporations across all industries to help collect, organize and analyze data to provide business insights. They work closely with stakeholders and must have good communication skills. Attention to detail, problem-solving, and critical thinking are also crucial.
AI Product Manager
AI product managers oversee the strategy, development, and marketing of AI products. They understand customer needs, define requirements and product vision, prioritize features, and work cross-functionally to launch successful AI-powered products.
To become an AI product manager, you need a bachelor’s degree in computer science, engineering or business. Take electives in AI and machine learning. Gain product management experience through internships. Build a strong technical foundation even as a PM.
AI product managers are employed by big tech firms like Google, Amazon, Microsoft, etc. as well as startups developing innovative AI solutions. They bridge the gap between the business and technical sides. Strong communication skills and product intuition are vital for this role.
Robotics Engineer
Robotics engineers research, design, develop, and test robotic systems and devices. They use principles from mechanical engineering, electrical engineering, computer science, and AI to create robots and automation solutions.
To become a robotics engineer, pursue an undergraduate degree in robotics, mechatronics, electrical engineering, mechanical engineering or computer science. Get hands-on experience through robotics competitions, clubs, or building your own robots. Learn skills like 3D modeling, prototyping, programming.
Robotics engineers find jobs at technology companies, manufacturing plants, research labs, and engineering firms. Growth areas include industrial automation, surgical robots, autonomous vehicles, drones, and consumer robots. Analytical skills and creativity are needed to solve real-world problems through robotics.
Computer Vision Engineer
Computer vision engineers develop AI systems that can process, analyze, and understand digital images and videos to extract information. They work on things like image classification, object detection, face recognition, and self-driving car vision systems.
To become a computer vision engineer, pursue a master’s degree focused in computer vision and machine learning after majoring in computer science or electrical engineering. Take advanced courses in AI, data analysis, and linear algebra. Work on computer vision projects using libraries like OpenCV and TensorFlow.
Computer vision engineers work at technology and engineering companies building innovative computer vision applications, like Snapchat’s lenses or Tesla’s self-driving system. This role combines software engineering with deep learning and image processing skills.
NLP Engineer
Natural language processing (NLP) engineers build systems that can understand, interpret, and generate human language. They develop AI models for speech recognition, language translation, sentiment analysis, text generation, and other NLP tasks.
To get into NLP engineering, study computer science, linguistics or a related field. Take electives covering topics like computational linguistics, deep learning, and statistics. Build NLP projects using Python libraries like NLTK, spaCy, and TensorFlow.
NLP engineering roles exist at large tech firms like Amazon, Google, Microsoft, etc. as well as startups working on conversational AI. Communication and problem-solving skills are vital. As an NLP engineer, you’ll get to work at the intersection of computer science and human language.
AI Consultant
AI consultants advise companies on strategies to identify, evaluate, and implement AI solutions to grow their business. They assess use cases, implement pilots, integrate AI systems, measure ROI, and help scale AI adoption.
To become an AI consultant, earn a bachelor’s degree in computer science or business. Get experience through internships in strategy consulting or technology consulting firms. Develop fluency in AI concepts and applications across industries.
Top firms hiring AI consultants include Deloitte, Accenture, EY, KPMG, etc. In this role, you get exposure to different business contexts. Strong communication skills and business acumen are essential. An analytical and creative mindset also helps deliver results.
AI Ethics Researcher
AI ethics researchers study the ethical implications of artificial intelligence systems and develop principles, guidelines, and tools to ensure AI is trustworthy and operates safely, ethically, and aligned with human values.
Pursuing a degree in computer science, cognitive science, philosophy, or law gives a solid foundation. Take courses in ethics and technology policy. Look for research opportunities at leading ethics institutes. Develop programming skills to prototypically test ideas.
AI ethics researchers are employed by organizations like the AI Now Institute, the Future of Humanity Institute, and more to advise on AI policy issues and assess new technologies. With an AI ethics focus, you can help shape the future development of AI to benefit society.
Conclusion
Artificial intelligence is transforming industries and creating many exciting career opportunities. As this technology continues to evolve rapidly, there will be an increasing need for talent with AI skills. The roles discussed in this article, from AI researchers to data scientists to machine learning engineers, offer diverse options to work with different aspects of AI.
To break into the field, focus on building up relevant technical skills, getting hands-on experience through projects and internships, and pursuing education in computer science, data science, or other related disciplines. Keep learning about new advancements in AI and direct your efforts towards roles aligning with your interests and strengths. With innovation in AI accelerating, an AI career offers the chance to work at the cutting edge and help shape the future.
With a passion for AI and its transformative power, Mandi brings a fresh perspective to the world of technology and education. Through her insightful writing and editorial prowess, she inspires readers to embrace the potential of AI and shape a future where innovation knows no bounds. Join her on this exhilarating journey as she navigates the realms of AI and education, paving the way for a brighter tomorrow.