History is full of examples of jobs that were predicted to be doomed by automation but that instead flourished and were transformed.
Artificial intelligence (AI) is a rapidly growing field that is transforming the way we live and work. AI occupations will increase in quantity, complexity, and diversity as the field develops. For a variety of professionals, including junior and senior researchers, statisticians, practitioners, experimental scientists, etc., this will open doors. The potential for lucrative employment possibilities is growing along with the demand for AI experts.
AI consultants help businesses leverage the potential of Artificial Intelligence and Machine Learning strategies. They help organizations understand how AI can bring transformation to their existing product and how their idea can be converted into reality.
AI consultants assist clients in comprehending and utilizing AI technologies. They collaborate closely with engineers and other consultants to assist clients in identifying new opportunities and formulating plans for utilizing AI to enhance business operations. AI consultants can expect to earn an average salary of around $80,000 to $100,000 per year.
AI Product Manager
Organizations in every industry are accelerating their use of artificial intelligence and machine learning to create innovative new products and systems. This requires professionals across a range of functions, not just strictly within the data science and data engineering teams, to understand when and how AI can be applied, to speak the language of data and analytics, and to be capable of working in cross-functional teams on machine learning projects.
The creation and administration of AI products fall under the purview of AI product managers. To find new opportunities and establish strategies for developing new AI products, they collaborate closely with engineers and data scientists. AI product managers can expect to earn an average salary of around $80,000 to $100,000 per year.
Business Development Manager
Business development managers are responsible for driving business growth within a company. They develop a network of contacts to attract new clients, research new market opportunities and oversee growth projects, making sales projections and forecasting revenue, in line with projected income.
Managers of AI business development are in charge of finding new business possibilities and formulating plans for expanding the company. In order to locate new markets and create alliances, they collaborate closely with other managers and executives. Business intelligence developers earn an average salary of $86,500, going up to $130,000 with experience.
AI Research Scientist
He or she is responsible for managing and setting up product infrastructure and AI development projects. Study and evolve data science prototypes. Implement machine learning algorithms and AI tools within research opportunities based on current parameters. Build AI models from scratch and assist in sharing knowledge of the model’s function.
They also conduct studies and create new AI technologies, AI research experts use machine learning and other AI approaches. They collaborate closely with other scientists and engineers to push the limits of what AI is capable of. Research scientists are in high demand and command an average salary of $99,800.
Deep Learning Engineer
A deep learning engineer is responsible for building and maintaining the algorithms that power Artificial Intelligence applications. These engineers must be able to work with various technologies, including machine learning, data science, artificial intelligence, and big data.
Deep learning technologists utilize these methods to give computers the ability to learn from massive amounts of data. To develop and execute sophisticated neural networks and other deep learning algorithms, they collaborate closely with data scientists and other technologists. The average salary of a software engineer is $108,000. This goes up to $150,000 based on your specialization, experience, and industry.
A robotics engineer designs prototypes, builds and tests machines, and maintains the software that controls them. They also conduct research to find the most cost-efficient and safest process to manufacture their robotic systems.
In other words, designers, developers, and testers of robotic systems are robotic engineers. They collaborate closely with other scientists and engineers to develop systems that are capable of carrying out a variety of operations, from manufacturing and assembling to surgery and search and rescue. The average salary of a robotics engineer is $87,000, which can go up to $130,000 with experience and specialization.
Natural Language Processing Engineer
This technology is one of the most broadly applied areas of machine learning and is critical in effectively analysing massive quantities of unstructured, text-heavy data. As AI continues to expand, so will the demand for professionals skilled at building models that analyse speech and language, uncover contextual patterns, and produce insights from text and audio.
By the end of this Specialization, you will be ready to design NLP applications that perform question-answering and sentiment analysis, create tools to translate languages and summarize text, and even build chatbots.
Engineers that specialize in natural language processing (NLP) employ machine learning and NLP methods to help computers comprehend and interpret human discourse. They collaborate closely with other engineers to create systems that can comprehend human voice and text and react accordingly, the average salary of an NLP engineer is $78,000, going up to over $100,000 with experience.
Computer Vision Engineer
They help computers comprehend and interpret visual data, computer vision engineers combine machine learning and computer vision approaches. To create systems that can detect and categorize photographs, movies, and other visual data, they collaborate closely with other engineers. Computer vision engineers can expect to earn an average salary of around $120,000 to $150,000 per year.
Data science is one of the hottest professions of the decade, and the demand for data scientists who can analyses data and communicate results to inform data driven decisions has never been greater.
Data Scientists help analyses huge data sets and glean insights, data scientists employ statistical and machine learning techniques. To find trends and patterns in data, they collaborate closely with business executives before using this knowledge to make data-driven decisions. The average salary of a data scientist is $105,000. With experience, this can go up to $200,000 for a director of data science position.
Machine Learning Engineer
Machine learning engineers act as critical members of the data science team. Their tasks involve researching, building, and designing the artificial intelligence responsible for machine learning and maintaining and improving existing artificial intelligence systems.
This job includes designing, creating, and implementing machine learning models are under the purview of machine learning engineers. To create and implement sophisticated algorithms and systems, they collaborate closely with software engineers and data scientists. The average salary of a machine learning engineer in the US is $1,31,000. Giant tech Organizations pay significantly higher—in the range of $170,000 to $200,000.
The impact of AI might not be linear but could build up at an accelerating pace over time. Its contribution to growth might be three or more times higher by 2030 than it is over the next five years. An S-curve pattern of adoption and absorption of AI is likely—a slow start due to the substantial costs and investment associated with learning and deploying these technologies, then an acceleration driven by the cumulative effect of competition and an improvement in complementary capabilities alongside process innovations.
It would be a misjudgment to interpret this “slow burn” pattern of impact as proof that the effect of AI will be limited. The size of benefits for those who move early into these technologies will build up in later years at the expense of firms with limited or no adoption.