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Top 10+ Jobs in AI and the Right Artificial Intelligence Skills You Need to Stand Out

In recent years, Artificial Intelligence (AI) technology has witnessed a surge in popularity, manifesting in various aspects of our daily lives, such as robots serving meals in restaurants and self-driving cars. Coined by American computer scientist John McCarthy, AI is defined as the scientific and engineering discipline dedicated to creating intelligent machines, particularly intelligent computer programs. The focus of AI lies in developing sophisticated software and systems that emulate human cognitive processes, including thinking, learning, decision-making, and problem-solving. This technology enables machines to execute tasks with human-like capabilities by learning from experience.

Concerns about the potential replacement of humans by robots persist among professionals globally. However, a study by Gartner contradicts this apprehension, indicating that AI is a burgeoning field poised to generate 2.8 million jobs by 2020. The term "AI" encompasses a wide range of technologies, including general artificial intelligence, machine learning, expert systems, data mining, and more. Presently, there is a high demand for AI capabilities across diverse industries such as gaming, robotics, face recognition software, weaponry, speech recognition, vision recognition, expert systems, and search engines.

Top Artificial Intelligence skills

The interdisciplinary realm of artificial intelligence (AI) integrates computer science, mathematics, engineering, and related disciplines. Within AI, various applications such as natural language processing, image recognition, robotics, and decision-making algorithms showcase its diverse uses.

One crucial skill is the ability to create algorithms for machine learning, involving learning from data to make predictions or judgments. Proficiency in programming languages like Python, R, and MATLAB, along with experience in machine learning frameworks like TensorFlow and Keras, is essential for this aspect.

Natural Language Processing (NLP) is another vital skill for developing AI systems that interact with humans using natural language. NLP encompasses understanding human language, processing and analyzing text, and generating responses that are both credible and natural. Proficiency in Python, Java, and familiarity with NLP tools like NLTK and spaCy is necessary for mastering NLP.

Data science skills are indispensable for handling large datasets in AI projects. These skills include data extraction, analysis, and visualization, requiring knowledge of programming languages such as Python, R, and SQL, as well as hands-on experience with tools like Pandas and NumPy.

A broad skill set is needed for creating and applying AI systems, including expertise in:

  1. Machine learning
  2. Natural Language Processing (NLP)
  3. Data science
  4. Deep learning
  5. Computing
  6. Robotics
  7. Problem-solving

Professionals in AI must possess programming proficiency in various languages and be familiar with frameworks and development tools. Acquiring and honing these skills not only provides a competitive advantage but also opens doors to exciting career opportunities as AI technologies continue to evolve.

1. Machine Learning Engineer

One of the most coveted roles in the field of AI, machine learning engineers are required to possess robust software skills, apply predictive models, and leverage natural language processing in handling extensive datasets. Proficiency in software development methodology, agile practices, and a comprehensive understanding of modern software development tools, ranging from IDEs such as Eclipse and IntelliJ to the components of a continuous deployment pipeline, is crucial. Embark on your journey as an AI/ML specialist through our AI & Machine Learning Bootcamp.

The average salary for this role is $121,106 (according to Glassdoor).

Preferred Qualifications: Companies seeking machine learning engineers typically prefer candidates with a master's or doctoral degree in computer science or mathematics, complemented by practical knowledge of contemporary programming languages like Python, Java, and Scala. Strong computer programming skills, advanced mathematical proficiency, familiarity with cloud applications and languages, effective communication, analytical prowess, and certifications in machine learning are highly valued by these organizations.

2. Robotic Scientist

Robotic scientists play a pivotal role in the automation of various tasks, ensuring the seamless functioning of robots. Their applications span diverse fields such as space exploration, healthcare, security, and numerous scientific domains. The primary responsibility involves designing mechanical devices or robots capable of executing tasks based on human commands. Proficiency in writing and manipulating computer programs, collaboration with other specialists, and the development of prototypes are essential skills in this role.

The average salary for this role is $83,241 (according to Glassdoor).

Preferred Qualifications: A bachelor's degree in robotic engineering, mechanical engineering, electro-mechanical engineering, or electrical engineering is a fundamental requirement. Companies seek professionals with specializations in advanced mathematics, physical sciences, life sciences, computer science, computer-aided design and drafting (CADD), physics, fluid dynamics, materials science, and related AI certifications.

3. Data Scientist

Data scientists employ machine learning and predictive analytics to gather, analyze, and interpret extensive data sets, uncovering insights that go beyond statistical analysis. Proficiency in Big Data tools like Hadoop, Pig, Hive, Spark, and MapReduce is essential for these experts. They are skilled in programming languages such as SQL, Python, Scala, and Perl, as well as statistical computing languages.

With an average salary of $117,345 (according to Glassdoor)

Data scientists are typically highly educated, often holding master's or doctoral degrees, though a computer science advanced degree is preferred. Key technical skills include expertise in SAS and R, Python coding, Hadoop, cloud tools like Amazon's S3, and handling unstructured data. Non-technical skills, including effective communication, analytical thinking, intellectual curiosity, and business acumen, are also crucial in this role.

4. Research Scientist

A research scientist excels in various artificial intelligence domains, spanning machine learning, computational statistics, and applied mathematics. Expertise includes deep learning, graphical models, reinforcement learning, computer perception, natural language processing, and data representation.

The average salary for this role is $83,490 (according to Glassdoor).

Essential qualifications encompass a master's or doctoral degree in computer science or a related technical field, or equivalent practical experience. Preferred skills involve parallel computing, artificial intelligence, machine learning, algorithm knowledge, distributed computing, and benchmarking. Additionally, a comprehensive understanding of computer architecture, along with strong verbal and written communication skills, is highly recommended for individuals pursuing a career in this field.

5. Business Intelligence Developer

Business intelligence developers, in high demand, specialize in analyzing intricate data to identify current business and market trends, enhancing organizational profitability and efficiency. Masters of technical and analytical skills, they excel in communication and problem-solving. Their responsibilities include designing, modeling, building, and maintaining data for complex, extensive, and highly accessible cloud-based platforms.

The average salary for this role is $90,430 (according to Glassdoor).

Preferred qualifications include a bachelor's degree in computer science, engineering, or a related field. Alternatively, a combination of certifications and on-the-job experience is desirable. Candidates with expertise in data warehouse design, data mining, BI technologies, SQL queries, SQL Server Reporting Services (SSRS), SQL Server Integration Services (SSIS), and renowned data science certifications are highly favored.

The job opportunities driven by artificial intelligence's advent are set to expand as technology continues to innovate. Gartner experts predict that "AI will create more jobs than it eliminates." Each role, however, requires education and training to meet industry needs. Raj Mukherjee, Senior Vice President of Product at Indeed, emphasizes standard technical requirements like a computer science degree or programming skills. Proficiency in programming languages such as Python, Java, C/C++, and experience in artificial intelligence, machine learning, or natural language processing are top skills sought by AI employers.

6. AI Product Manager

An AI product manager oversees the development, application, and management of AI-based goods and services, ensuring they align with corporate objectives and meet client demands. Collaborating with cross-functional teams, including software developers, data scientists, and business stakeholders, is crucial for success.

The average salary for this role is $120,171 (according to Glassdoor).

Required Qualifications: Typically, AI product managers have a background in computer science, data science, or engineering. While many employers prefer candidates with a master's degree, some may consider those with a bachelor's degree in a related field. Proficiency in software development, project management, and AI technologies is essential.

7. AI Consultant

An individual specializing in advising companies on the development and implementation of AI-based solutions is termed an AI consultant. The role involves collaborating with clients to understand their business needs and devising customized AI solutions to address complex challenges.

The average salary for this role is $100,512 (according to Glassdoor).

Required Qualifications: Typically, a bachelor's or master's degree in a relevant field like computer science, data science, or engineering is necessary for AI consultants. Proficiency in AI technologies, previous experience in software development, data analysis, or consulting is commonly expected. Essential skills include excellent communication, leadership, and problem-solving abilities, along with the capability to work with diverse teams and manage multiple tasks concurrently. Possessing certifications in AI technologies is an additional advantage.

8. Robotics Engineer

A robotics engineer is an expert in creating, developing, and managing robots and robotic systems. Specializing in robotics, these engineers typically engage in market research, analyze customer needs, design and build robotic systems, conduct testing and troubleshooting, and consistently enhance system performance.

The average salary for this role is $99,053 (according to Glassdoor).

Required Qualifications: Typically, a bachelor's or master's degree in mechanical engineering, electrical engineering, or robotics is necessary for aspiring robotics engineers. Prior experience in robotics or a related field, such as mechatronics, is usually required. Robotics engineers should excel in computing, control systems, mechanical design, and robotics design. Collaborative teamwork, strong problem-solving abilities, and effective communication skills are essential. Some companies may prefer candidates with certifications in robotics.

9. NLP Engineer

An NLP (Natural Language Processing) engineer specializes in creating and implementing formulas and models to enable computers to understand, interpret, and generate human language. Core responsibilities include researching and applying NLP techniques, developing and refining NLP models, and integrating NLP systems into software programs.

The average salary for this role is $117,534 (according to Glassdoor).

Required Qualifications: Typically, a bachelor's or master's degree in computer science, data science, or a related field is required for individuals aspiring to become NLP engineers. Prior knowledge of NLP or related areas such as artificial intelligence, machine learning, or data mining is often a prerequisite. NLP engineers should possess technical expertise in machine learning, statistical analysis, and NLP algorithms and models. Effective collaboration within cross-functional teams, along with strong problem-solving and communication skills, is essential. Some employers may prioritize candidates with certifications in NLP or related fields.

10. Research Assistant

In the realm of artificial intelligence research, a research assistant is typically responsible for collecting and analyzing data, testing novel algorithms, and contributing to the development of AI-utilizing applications. Additionally, research assistants may be tasked with producing summaries of studies, academic papers, and presentations based on their research findings.

The average salary for this role is $90,430 (according to Glassdoor).

Required Qualifications: Generally, a bachelor's or master's degree in computer science, artificial intelligence, or a closely related field is a prerequisite for a research assistant role in this domain. Alongside proficiency in data analysis, programming, and research methods, previous research experience in AI or machine learning is highly valued. Strong technical skills encompassing data analysis, research methodologies, and programming languages like Python or R are essential. Effective verbal and written communication, coupled with the ability to collaborate within cross-functional teams, is also crucial.

11. Deep Learning Engineer

A deep learning engineer is responsible for developing and implementing deep learning algorithms to address intricate challenges across diverse sectors such as finance, healthcare, and autonomous vehicles. They specialize in constructing deep neural networks capable of assimilating vast amounts of information.

The average salary for this role is $137,474 (according to Glassdoor).

Required Qualifications: Typically, a bachelor's or master's degree in computer science, data science, or a related field is necessary for aspiring deep learning engineers. Alongside this, valuable qualifications include prior experience in machine learning, programming, data analysis, and the development and implementation of deep learning algorithms. Proficiency in technical aspects, including deep learning frameworks like TensorFlow or PyTorch, familiarity with programming languages such as Python or C++, and expertise in cloud computing platforms is essential. Strong problem-solving skills and the ability to collaborate effectively within cross-functional teams are also crucial attributes.

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