AI Position Titles, Roles, and How to Land One
By
Liz Fujiwara
•
Sep 3, 2025
Automated intelligence jobs are rapidly gaining importance as organizations across industries increasingly adopt AI technologies to streamline operations, improve decision-making, and drive innovation. Roles such as AI engineers, data scientists, and machine learning specialists are at the forefront of this transformation, powering intelligent systems that analyze data, automate processes, and generate actionable insights.
This article provides an in-depth look at these critical AI roles, outlining the responsibilities, required skills, and career pathways for those looking to start or advance their careers in this dynamic and fast-growing field. With the demand for AI expertise continuing to rise across sectors like healthcare, finance, technology, and retail, understanding these roles and the skills needed to excel in them has never been more essential. Now is the perfect time to explore jobs in AI and position yourself for success in this booming industry.
Key Takeaways
The demand for AI professionals is expected to grow substantially, with an estimated 377,500 new jobs created annually and a 26% increase in computer and information technology occupations projected between 2023 and 2033.
Key AI roles include AI Engineer, Machine Learning Engineer, Data Scientist, NLP Engineer, and Robotics Engineer, each offering lucrative salaries and playing pivotal roles in the development and implementation of AI technologies.
Educational pathways such as degrees in computer science, data science, or related fields, combined with relevant certifications and hands-on experience through internships or personal projects, are essential for building a successful career in automated intelligence.
Understanding Automated Intelligence Jobs
Artificial intelligence (AI) involves the simulation of human intelligence in machines, enabling them to perform tasks that typically require human cognition, such as reasoning, learning, and problem-solving. This rapidly growing field offers a wide array of AI careers, each with distinct roles and responsibilities. The surge in demand for AI professionals is fueled by increasing access to data, with roughly 97 million new AI-related jobs projected by 2025.
The U.S. Bureau of Labor Statistics forecasts a 26% growth in computer and information technology occupations from 2023 to 2033, highlighting the strong need for skilled AI experts. Beyond driving technological innovation, AI is expected to have a significant economic impact, contributing an estimated $15.7 trillion by 2030.
As more industries integrate AI technologies, the job market for AI professionals continues to expand, offering positions ranging from entry-level roles to high-level strategic positions across nearly every sector. The diversity of AI careers accommodates a broad range of skills and expertise, making it an exciting and accessible field for both newcomers and experienced professionals alike.
Popular Automated Intelligence Job Titles
The world of automated intelligence encompasses a wide range of specialized roles, each critical to advancing AI applications. Key positions include:
AI Engineers
Machine Learning Engineers
Data Scientists
Natural Language Processing (NLP) Engineers
Robotics Engineers
These roles are pivotal in designing, developing, and implementing AI solutions that drive innovation across industries. They offer lucrative career prospects, with average salaries ranging from $88,593 to $119,668, reflecting the high demand for skilled professionals.
Understanding the specific responsibilities of each position provides insight into their importance within the AI ecosystem and highlights the diverse skill sets required to excel in this rapidly evolving field.
AI Engineer
AI Engineers and software engineers are at the forefront of developing tools, systems, and processes for real-world AI applications. They focus on solving complex problems using artificial intelligence and are responsible for creating and maintaining AI models and algorithms, driving innovation as industry-leading AI professionals.
With an average salary of approximately $136,366, AI Engineers are highly sought after for their expertise in integrating AI solutions across various industries.
Machine Learning Engineer
Machine Learning Engineers are pivotal in the AI landscape, responsible for:
Researching, building, and designing AI systems.
Collaborating closely with data scientists to create and refine machine learning algorithms, ensuring efficiency and effectiveness.
Maintaining and optimizing existing AI systems.
Working with other professionals to enhance AI applications and implement solutions across industries.
The average salary for a Machine Learning Engineer is $162,509, reflecting the strong demand and value of their specialized expertise.
Data Scientist
Data Scientists and Research Scientists play a crucial role in analyzing large datasets to extract meaningful patterns and actionable insights. They frequently utilize machine learning algorithms and data mining techniques to improve their analyses, supporting data-driven decisions that influence business strategies.
With an average salary of $112,590, Data Scientists are indispensable in industries that rely on data for strategic decision-making. Their expertise in data analysis, modeling, and visualization makes them key contributors to the AI ecosystem.
Natural Language Processing Engineer
Natural Language Processing (NLP) Engineers specialize in developing systems that can process and understand human language, including applications like chatbots, translation services, and sentiment analysis. They design and implement NLP solutions using AI technologies for tasks such as speech recognition, text analytics, and language modeling.
The average salary for NLP Engineers is $159,279, reflecting the specialized expertise and technical skills required for this role.
Robotics Engineer
Robotics Engineers are responsible for designing, developing, and implementing robotic systems across a variety of industries. Their work includes creating new products, overseeing prototypes, and ensuring that robotic applications operate efficiently and reliably.
With an average salary of $113,004, Robotics Engineers combine expertise in mechanical, electrical, and software engineering with AI technologies to develop innovative and effective robotic solutions.
Emerging Roles in Automated Intelligence
As AI technologies advance, the landscape of careers in automated intelligence continues to evolve rapidly. The impact of AI is particularly pronounced in data-rich sectors, where job functions are shifting to accommodate new technological capabilities. Emerging roles are being created to address evolving needs, such as managing vast datasets, ensuring ethical AI deployment, and enhancing user experiences with intelligent systems.
Some of these emerging positions include Big Data Analysts, UX Developers, and AI Ethicists, each offering unique responsibilities and opportunities for professionals to contribute to the AI-driven economy. Let’s explore these emerging roles in more detail.
AI Product Manager
AI Product Managers play a pivotal role in guiding the development and deployment of AI products. They tackle complex challenges for large organizations, ensuring that AI solutions address market needs while aligning with overarching business strategies. Their responsibilities include coordinating cross-functional teams, managing project timelines, and overseeing the successful launch of AI initiatives.
A key aspect of their role is aligning AI product initiatives with business objectives. By bridging the gap between technical teams and business stakeholders, AI Product Managers ensure that AI products deliver tangible value and support the organization’s strategic goals.
AI Ethics Specialist
AI Ethics Specialists are responsible for ensuring that AI systems are developed and deployed in a responsible and ethical manner. They focus on addressing challenges such as bias, fairness, transparency, and privacy, working to identify and mitigate potential ethical issues in AI applications. This role demands a deep understanding of both technology and human behavior to create AI solutions that are equitable and compliant with regulations.
The significance of AI Ethics Specialists is paramount, as they help organizations implement fair and unbiased AI systems while upholding ethical standards. Their work fosters trust, accountability, and social responsibility in AI technologies, making them indispensable contributors to the responsible advancement of the field.
AI Hardware Engineer
AI Hardware Engineers specialize in designing and developing specialized hardware that powers AI applications, prioritizing high performance and energy efficiency. They work closely with software teams to optimize AI models and applications, ensuring seamless integration between hardware and software components. This collaboration is essential for building AI systems that are both efficient and effective.
With the rapid evolution of AI technologies, the demand for skilled AI Hardware Engineers is steadily increasing, making it a highly promising career path. Their expertise in creating and optimizing hardware tailored for AI workloads is crucial to advancing the capabilities and performance of modern AI systems.
Skills Needed for Automated Intelligence Jobs
The skills required for careers in automated intelligence are diverse and multifaceted, encompassing:
Proficiency in programming languages, essential for developing AI models and applications
Expertise in machine learning techniques, enabling the creation of intelligent systems
Strong data analysis capabilities, critical for deriving insights and informing AI solutions
Ethical awareness, to ensure responsible and unbiased AI development
Interpersonal skills, as AI professionals must communicate complex technical concepts to stakeholders across different teams
Let’s explore these skills in more detail to understand what it takes to succeed in the AI field.
Programming Languages
Developing AI models and applications requires proficiency in several programming languages, including:
Python: Widely used due to its simplicity and extensive libraries tailored for AI and machine learning tasks.
R: Commonly used for statistical analysis and data visualization in AI projects.
SQL: Essential for managing and querying large datasets used in AI systems.
C: Crucial for specific AI applications, particularly in research and academia.
MATLAB: Important for specialized AI applications, especially in research and scientific computing.
Familiarity with these programming languages and relevant frameworks demonstrates strong technical expertise. Mastering these languages enables the development of complex AI systems and highlights a candidate’s adaptability to different AI tools and technologies. This technical proficiency is a fundamental requirement for nearly every AI role.
Machine Learning Techniques
Understanding machine learning concepts and algorithms is critical for any AI career. This includes knowledge of:
Supervised learning methods
Unsupervised learning methods
Reinforcement learning methods
These techniques are essential for developing and refining AI models. Machine learning engineers often employ them to create predictive models and enhance the accuracy and efficiency of AI applications.
Proficiency in machine learning is not just theoretical. It requires practical application. AI professionals must be capable of implementing these algorithms to solve real-world problems, which demands a deep understanding of the underlying principles and the ability to adapt them to specific use cases.
Data Analysis and Visualization
Data analysis and data visualization are crucial skills for AI professionals. Proficiency in programming languages such as R and Python is beneficial for effective data analysis, enabling the extraction of meaningful patterns from large datasets.
Additionally, understanding statistical methods and using data presentation tools like Tableau and Power BI enhances the ability to communicate insights clearly and effectively. These skills facilitate statistical analysis, business intelligence, and data-driven decision-making.
Educational Pathways for a Career in Automated Intelligence
Embarking on a career in automated intelligence often begins with a strong educational foundation. Common degrees for AI roles include computer science, mathematics, and data science. While a bachelor’s degree is typically the minimum requirement, advanced degrees such as a master’s in data science or AI can greatly increase job opportunities and earning potential.
Bachelor's Degree in Computer Science
A bachelor’s degree in computer science provides a strong foundation for a career in AI. This degree equips students with essential programming skills and a thorough understanding of algorithms, both of which are crucial for developing automated intelligence applications.
The knowledge gained from a computer science degree is directly applicable to a variety of AI roles, making it an important starting point for aspiring AI professionals.
Master's Degree in Data Science
Pursuing a master’s degree in data science can greatly enhance a candidate’s expertise and career prospects in AI. An advanced degree in data science provides a deep understanding of complex data manipulation and predictive modeling techniques. This degree not only opens doors to high-level AI roles but also increases earning potential and job security.
Certifications and Online Courses
Certifications and online courses are essential for validating skills and knowledge in AI roles. Platforms like Coursera, edX, and Udacity offer specialized courses that help individuals acquire key AI and machine learning competencies. These certifications enhance marketability by demonstrating expertise and a commitment to continuous learning in the competitive AI field.
Gaining Practical Experience
Gaining practical experience is crucial for building a successful career in automated intelligence. Entry-level AI positions such as AI Intern, Junior Data Analyst, and Machine Learning Intern offer valuable on-the-job training that develops real-world skills. Participating in AI projects, competitions, and hackathons can further enhance practical knowledge. Networking through AI conferences, workshops, and online communities also helps in discovering opportunities and connecting with industry professionals.
Internships
Internships provide invaluable real-world experience for aspiring AI professionals. At companies like C3 AI, interns work on client-focused projects, gaining hands-on exposure to practical AI applications. These programs often include mentorship, incentives for completing self-learning courses, and access to professional networks, all of which enhance skill development and career growth. Internships serve as a critical stepping stone for building expertise and establishing a foothold in the AI industry.
Personal Projects
Developing personal projects is an effective way to demonstrate skills, creativity, and initiative to potential employers. Showcasing projects that involve AI or machine learning solutions highlights practical experience and problem-solving capabilities. Additionally, contributing to collaborative projects emphasizes teamwork, communication, and technical documentation skills, all of which are highly valued in the AI industry.
Open-Source Contributions
Participating in open-source projects provides hands-on experience and fosters collaboration with the global AI community. Contributing to these projects enhances technical skills, demonstrates initiative, and strengthens a professional portfolio, making individuals more competitive in the AI job market. Additionally, active engagement in the open-source community helps build valuable networks, facilitates knowledge sharing, and offers opportunities to learn from experienced professionals in the field.
How Companies are Using AI Responsibly in Hiring
Companies are increasingly adopting AI technologies to streamline hiring processes while striving to maintain transparency, fairness, and efficiency. AI-powered recruitment tools can automate candidate screening, match applicants to suitable roles, and even assist in evaluating qualifications, reducing manual workload and minimizing human bias. These technologies allow organizations to make faster, more data-driven hiring decisions while improving the candidate experience.
AI-Powered Recruitment Tools
AI recruitment tools offer several key capabilities:
Analyze candidate responses during video interviews, evaluating facial expressions, tone, and body language to assess potential job performance.
Streamline the hiring process by automating candidate screening and matching to relevant roles.
Provide objective assessments that help reduce unconscious bias in hiring decisions.
Ultimately, these AI-powered tools improve the alignment between candidates and job positions, enhancing overall hiring efficiency and decision-making.
Speeding Up the Hiring Timeline
AI recruitment platforms like Fonzi can:
Automate resume screening, significantly reducing time spent on manual evaluations.
Evaluate resumes efficiently by identifying relevant keywords.
Expedite the candidate selection process.
By leveraging AI-driven tools, HR teams can concentrate on qualified candidates, making the hiring process more efficient for both employers and applicants.
Preparing for Interviews in Automated Intelligence
Preparing for interviews in automated intelligence is essential because of the competitive job market and the need to differentiate yourself. Effective strategies include understanding common interview questions, reviewing relevant technical skills, and highlighting personal projects and real-world experience.
Showcasing Technical Skills
Showcasing technical skills during interviews is essential for standing out in the competitive field of automated intelligence. Key technical skills include proficiency in programming languages, machine learning techniques, and data analysis and visualization.
Combining technical skills with hands-on experience significantly strengthens your candidacy. Demonstrating the application of these skills in real-world scenarios helps make a strong impression on potential employers and highlights your readiness for AI roles.
Practicing Common Interview Questions
Preparation is essential for success in competitive automated intelligence job interviews. Practicing common interview questions helps candidates clearly convey their skills, experiences, and problem-solving abilities through strategic thinking.
Highlighting involvement in real-world projects and demonstrating proficiency in relevant programming languages and machine learning techniques are key strategies for making a strong impression on interviewers.
AI Position Titles, Roles, and How to Land One
Securing a position in AI involves understanding the specific requirements and skills associated with each role. Common AI job titles include:
AI Engineer
Machine Learning Engineer
Data Scientist
NLP Engineer
Each of these positions has distinct responsibilities and demands a unique set of technical and analytical skills.
If you are struggling to find an AI job, consider pursuing internships, freelance projects, or participating in hackathons to gain practical experience. Aligning your skills and hands-on experience with the requirements of these roles can significantly increase your chances of landing a position in the AI field.
Summary
AI career opportunities require a distinct set of skills and expertise. From understanding job titles and responsibilities to gaining practical experience and preparing for interviews, this journey is both challenging and rewarding. By equipping yourself with the right knowledge, skills, and strategies, you can successfully navigate the AI job market and secure your desired role. Continuous learning and adaptability remain essential for thriving in this dynamic and evolving field.
