A Guide to AI Job Titles
The artificial intelligence (AI) landscape is evolving at a breakneck pace, and with it comes a plethora of new job titles and roles. If you're a company looking to hire AI talent or a professional aiming to break into the field, the sheer variety of positions can be overwhelming. From Applied Researchers to MLOps Engineers, understanding who does what is crucial for making informed decisions.
At People in AI, we've made it our mission to simplify this complex terrain. This guide breaks down the most common AI job titles you'll encounter, shedding light on their responsibilities and how they contribute to the broader AI ecosystem.
Table of Contents
- Applied Researcher
- Applied Scientist
- Machine Learning Engineer (MLE)
- MLOps Engineer
- Data Engineer
- Data Scientist
- AI Ethicist
- AI Product Manager
- Computer Vision Engineer
- Natural Language Processing (NLP) Engineer
- Deep Learning Engineer
- AI Architect
- AI Consultant
- AI Software Engineer
- Common Misconceptions About AI Job Titles
- How AI Job Titles Differ Across Industries
- Why Partner with People in AI
Applied Researcher
Role Overview: Applied Researchers focus on bridging the gap between theoretical AI research and practical applications. They explore new algorithms and models, often publishing their findings in academic journals.
Key Responsibilities:
- Conducting experiments to validate new AI theories.
- Collaborating with academic institutions.
- Translating research findings into real-world solutions.
Applied Scientist
Role Overview: Applied Scientists apply scientific principles to develop and enhance products or technologies. They work closely with engineering teams to implement AI models into production systems.
Key Responsibilities:
- Developing scalable AI models.
- Performing data analysis to improve algorithms.
- Working cross-functionally with product teams.
Machine Learning Engineer (MLE)
Role Overview: MLEs are the backbone of AI implementation. They design, build, and deploy machine learning models, ensuring they function efficiently in a production environment.
Key Responsibilities:
- Building data pipelines.
- Optimizing algorithms for performance.
- Integrating models into existing systems.
MLOps Engineer
Role Overview: MLOps Engineers focus on the operational aspects of AI models. They ensure seamless deployment, monitoring, and maintenance of machine learning models in production.
Key Responsibilities:
- Automating model deployment processes.
- Monitoring model performance.
- Implementing continuous integration and delivery (CI/CD) for ML models.
Data Engineer
Role Overview: Data Engineers are responsible for designing and maintaining the infrastructure that supports data storage and retrieval. They make sure data is accessible and usable for data scientists and analysts.
Key Responsibilities:
- Building and maintaining data warehouses.
- Ensuring data quality and integrity.
- Developing data acquisition pipelines.
Data Scientist
Role Overview: Data Scientists analyze and interpret complex data to help organizations make informed decisions. They use statistical methods and machine learning to extract insights.
Key Responsibilities:
- Performing exploratory data analysis.
- Building predictive models.
- Communicating insights to stakeholders.
AI Ethicist
Role Overview: AI Ethicists ensure that AI technologies are developed and used responsibly. They address ethical concerns like bias, fairness, and transparency.
Key Responsibilities:
- Conducting ethical risk assessments.
- Developing guidelines for responsible AI use.
- Collaborating with legal and compliance teams.
AI Product Manager
Role Overview: AI Product Managers oversee the development and launch of AI-driven products. They align AI capabilities with business objectives.
Key Responsibilities:
- Defining product roadmaps.
- Coordinating between technical and non-technical teams.
- Monitoring product performance and user feedback.
Computer Vision Engineer
Role Overview: Specialists in processing and interpreting visual data, Computer Vision Engineers develop algorithms that enable machines to "see."
Key Responsibilities:
- Developing image recognition models.
- Working on object detection and segmentation.
- Implementing real-time video analysis.
Natural Language Processing (NLP) Engineer
Role Overview: NLP Engineers focus on enabling machines to understand and interpret human language.
Key Responsibilities:
- Building language models.
- Implementing sentiment analysis.
- Developing chatbots and virtual assistants.
Deep Learning Engineer
Role Overview: Deep Learning Engineers specialize in neural networks and advanced algorithms to solve complex problems.
Key Responsibilities:
- Designing deep learning architectures.
- Training models on large datasets.
- Optimizing models for performance and scalability.
AI Architect
Role Overview: AI Architects design the overall AI strategy and architecture for organizations.
Key Responsibilities:
- Defining AI technology stacks.
- Ensuring scalability and reliability.
- Aligning AI initiatives with business goals.
AI Consultant
Role Overview: AI Consultants advise organizations on how to leverage AI technologies effectively.
Key Responsibilities:
- Assessing organizational needs.
- Recommending AI solutions.
- Providing implementation strategies.
AI Software Engineer
Role Overview: AI Software Engineers integrate AI functionalities into software applications.
Key Responsibilities:
- Developing AI-powered software.
- Ensuring seamless integration with existing systems.
- Testing and debugging AI components.
Common Misconceptions About AI Job Titles
Unfortunately, job titles in AI are far from standardized, leading to confusion for both employers and candidates. Misunderstanding these roles can cause mismatched expectations, flawed hiring decisions, or overlooked opportunities.
Some of the most common misconceptions include:
“All Data Scientists are AI experts.”
While data scientists work with data modeling and analytics, not all are trained in deep learning or cutting-edge AI frameworks. Many specialize in statistics or business analysis rather than advanced AI applications.
“AI Engineers and ML Engineers are the same.”
These roles overlap but differ in scope. ML Engineers focus on designing, training, and deploying machine learning models, while AI Engineers often take a broader, systems-level approach, integrating ML with natural language processing, computer vision, robotics, and overall product architecture.
“AI job titles are standardized across companies.”
In reality, one company’s “AI Specialist” may be another’s “Machine Learning Scientist.” This lack of uniformity creates confusion for applicants trying to match their skills with openings, and for recruiters trying to compare candidates.
“Ethical and governance roles are optional.”
With stricter regulations and growing public concern around AI bias, privacy, and fairness, roles focused on AI ethics, governance, and compliance are no longer nice-to-have— they’re becoming essential parts of responsible AI teams.
By clarifying these distinctions, companies can write more accurate job descriptions, and professionals can better position themselves for roles that match their expertise. This alignment not only saves time but also improves hiring quality and career satisfaction on both sides.
How AI Job Titles Differ Across Industries
AI roles don’t exist in a vacuum. They evolve based on the unique challenges of each industry. While foundational positions like Data Scientist, AI Engineer, and Machine Learning Engineer appear across most sectors, many organizations adapt these titles to reflect domain-specific expertise. This encourages professionals to not only bring technical skills but also a deep understanding of the field they’re working in.
For example:
Healthcare
AI is revolutionizing diagnostics and treatment planning, which has given rise to roles such as Clinical AI Specialist, Medical Imaging Engineer, and Healthcare Data Scientist. These professionals combine AI expertise with knowledge of medical standards, patient data privacy, and compliance regulations.
Finance
In banking and insurance, AI is used for fraud detection, algorithmic trading, and risk assessment. That’s why titles like Quantitative AI Analyst, Risk Modeling Engineer, and Financial Data Scientist are common. Here, success requires both strong AI skills and familiarity with financial markets and regulatory frameworks.
Retail & E-commerce
To power recommendation engines, demand forecasting, and personalized shopping, companies often hire Personalization Algorithm Engineers or E-commerce Data Scientists. These roles emphasize consumer behavior insights as much as machine learning models.
Manufacturing & Logistics
Predictive maintenance, robotics, and supply chain optimization drive the need for roles like Industrial AI Engineer or Smart Manufacturing Data Scientist. Domain knowledge of equipment and production workflows is crucial here.
Government & Public Sector
Public agencies are increasingly adopting AI for citizen services, cybersecurity, and policy modeling, leading to roles like AI Policy Analyst or Civic Data Scientist. These positions require navigating ethical and governance considerations alongside technical expertise.
Recognizing how AI job titles shift across industries helps companies recruit professionals who bring the right mix of technical mastery and domain knowledge, and helps candidates position themselves for roles that fit both their skills and interests.
Why Partner with People in AI
Navigating the AI talent landscape can be daunting. That's where we come in. At People in AI, we specialize in connecting businesses with top-tier AI professionals across North America.
What Sets Us Apart:
- Expertise: With a focus solely on AI and machine learning roles, we understand the nuances of each position.
- Network: Our extensive network allows us to find the perfect match for your needs.
- Customization: We tailor our services to fit your specific requirements, ensuring a seamless hiring process.
Conclusion
The AI industry is rich with opportunities but also fraught with complexities. Understanding the various roles and how they fit into your organization's goals is the first step toward success. Whether you're a company looking to build an AI team or a professional aiming to find your niche, knowing these roles can guide your journey.
Ready to find the right AI talent or position? Contact People in AI today and let us help you navigate the future.
