AI Position Titles, Roles, and How to Land One
By
Liz Fujiwara
•
Automated intelligence jobs are becoming central to how modern organizations operate and compete. As companies adopt AI to streamline processes, improve decision-making, and unlock new capabilities, roles like AI engineers, data scientists, machine learning specialists, and AI product managers are driving this transformation. These professionals build and deploy systems that analyze data, automate workflows, and generate meaningful insights at scale.
With over 70 percent of organizations now using AI in at least one business function, demand for skilled talent continues to surge across industries like healthcare, finance, technology, and retail. In this article, we’ll break down the key AI roles, the skills required to succeed, and the career paths available so you can position yourself in one of the fastest-growing fields in tech.
Key Takeaways
Demand for AI professionals is rapidly growing, with ~377,500 new jobs annually and a 26% increase in tech roles projected from 2023 to 2033
Over 225,000 AI jobs are currently available in the U.S.
Key roles include AI Engineer, Machine Learning Engineer, Data Scientist, NLP Engineer, and Robotics Engineer
Degrees in computer science or related fields, plus certifications and hands-on projects, are essential
AI roles consistently rank among the top jobs, especially ML engineers and data scientists
The U.S. Bureau of Labor Statistics projects ~356,700 openings annually in related fields
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. Applied mathematics and linear algebra are essential in developing algorithms, mathematical modeling, and control systems for robotics and AI technologies.
AI plays a significant role in robotics by integrating sensors, movement algorithms, and perception technologies.
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 responsibly and ethically. 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 a broad range of AI skills. Developing practical AI skills is crucial, including proficiency in programming languages such as Python, C++, Java, and R, as well as familiarity with machine learning (ML) frameworks like PyTorch and TensorFlow. These AI skills also cover technical knowledge in machine learning, deep learning, AI security, deployment techniques, and overall AI literacy. Key areas include:
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 are 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
Practical skills and a solid portfolio are increasingly favored over formal credentials for applied AI roles.
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
Deep learning architectures like CNNs (for vision) and RNNs (for sequential data) are key to advanced AI systems. Tools like TensorFlow and PyTorch are widely used to build and improve models. Beyond theory, AI professionals must apply these techniques in real-world scenarios, adapting them to specific problems and improving performance.
Data Analysis and Visualization
Data analysis and data visualization are crucial skills for AI professionals. Expertise in data engineering and data structures is essential for building and maintaining robust data systems and pipelines that support AI workflows. Proficiency in programming languages such as R and Python is beneficial for effective data analysis, enabling the extraction of meaningful patterns from large datasets. Apache Spark is a key tool for processing and analyzing large datasets in distributed environments.
Data analysis is at the heart of AI, as clean and interpretable data is crucial for building accurate models.
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.
Fonzi: Responsible AI, Match Day Speed, and Bias-Free Hiring
Fonzi brings responsible AI directly into the hiring workflow by combining automation with human oversight. Instead of relying on black-box decisions, Fonzi uses AI to structure and prioritize candidate evaluation while ensuring recruiters stay in control of final decisions. The platform is designed to eliminate bias in recruitment by focusing on demonstrated skills, technical impact, and real-world experience rather than traditional pedigree signals like school or past employers.
Fonzi also accelerates hiring through Match Day, where weeks of sourcing and screening are condensed into a single high-intent event. Companies connect with pre-vetted candidates who are already aligned on role requirements, seniority, and compensation, dramatically reducing time-to-hire. By blending speed, transparency, and fairness, Fonzi enables teams to scale hiring efficiently while building stronger, more diverse engineering organizations.
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 roles are now central to how companies innovate and compete, driving strong demand for positions like AI engineers, machine learning engineers, and data scientists. These professionals build and deploy systems that analyze data, automate processes, and generate insights across industries. Success in this field requires solid skills in programming, machine learning, and data analysis, along with hands-on experience through real projects. Emerging roles in areas like AI ethics and product management are expanding opportunities even further.
For candidates, a strong portfolio and practical problem-solving are key to standing out, while companies are increasingly using structured, skills-based hiring approaches. As AI adoption grows, continuous learning and adaptability remain essential to building a successful career in this fast-moving field.
FAQ
What are the most common AI position titles in companies?
How do I know which AI position fits my background?
What does an AI product manager do compared to an ML engineer?
How technical are most AI positions?
What’s the career growth path for an AI position?
