Today, AI is all around us and in places where we may not even realize it. The AI revolution has brought profound changes around the world, offering unparalleled opportunities for innovation and growth. A career in AI offers flexibility and diverse growth opportunities, making it a versatile career choice. The AI field provides many career prospects, where one can take on various roles and responsibilities depending on interest, experience, and position. The two most popular job roles in AI are AI Research Scientist and AI Engineer. In this post, let’s discuss these two most exciting career paths.
What is AI Researcher?
AI Researcher is an expert in various disciplines, including deep learning, statistics, machine learning, and mathematics. The role focuses on developing AI systems or researching how they can be implemented.
Depending on the task in hand, an AI Researcher –
- Solves engineering problems or
- Develops algorithms
- Research on problems that AI can solve
- Focuses on ethics, testing, or legal ramifications of AI.
The role of a research scientist is one of the most academically driven careers in AI. Most AI Researchers work in a lab or office, depending on whether they work at a university’s research center, a commercial company that researches innovative products or services, or for the government.
What is AI Engineer?
AI engineers are the creative minds behind AI systems. They are responsible for designing and installing AI-based models. They are skilled in various subjects, including data analytics, programming, and machine learning, which help them create models. These models learn from the provided data and make decisions. An AI Engineer must be proficient in programming languages, including Python, R, C++, Java, JavaScript, etc.
In addition to these technical skills, AI engineers must possess soft skills like communication and critical thinking, adaptability to continuous learning, domain knowledge, etc.
AI engineering is growing rapidly and has immense potential for individuals with the required knowledge and skills. With the right blend of non-technical and technical skills, an AI engineer will not only excel in his job but also contribute to advancing revolutionary AI solutions.
AI Researcher vs AI Engineer: Key Differences
AI Researchers and AI engineers work closely to build usable products for clients. They collaborate to develop AI solutions that work efficiently and accurately in real-world situations. However, there are certain key differences in these roles.
Let us look at the comparison in detail, which will help you make an informed choice to pursue your career goal.
An AI Researcher focuses mainly on designing and creating AI models, Algorithms, and techniques to solve complex problems. They conduct research and develop new concepts, theories, and models in a specific field. They design and train ML models and implement feature engineering and data preprocessing techniques to make accurate predictions and decisions.
On the other hand, AI engineers focus on applying engineering and scientific principles to develop solutions to practical issues. He implements and deploys AI models at scale and works on integrating and optimizing AI models into existing platforms. They team up with other AI professionals to ensure the resourceful running of AI models.
Required Skills
The skills required for these two roles differ substantially.
- AI researchers have diverse skills and strong computer science, statistics, and mathematics backgrounds.
- They excel in applying ML techniques and algorithms to real-world problems. Their in-depth knowledge of unsupervised, supervised, and deep learning allows them to optimize the best model for a given task.
- Familiar with various Big Data, AI, ML Algorithms
- They have experience developing innovative techniques and excellent domain knowledge. This allows them to develop and design AI systems to address the specific challenges and needs of a particular field or industry.
- Have excellent analytical and problem-solving skills to identify suitable AI algorithms for specific problems.
- Have Strong collaboration and communication skills.
In contrast, an AI Engineer has a background in Data Engineering, Software engineering, and Computer science. They blend their software engineering expertise with their AI algorithms knowledge to help stakeholders understand the results. An AI engineer must be:
- Proficient in programming languages, including C++, Java, R, and Python, to develop software applications and prototypes.
- Knowledge of cloud platforms to deploy and maintain AI systems in cost-effective environments.
- They must be familiar with AI frameworks and libraries and understand how to effectively build models by leveraging their knowledge.
- As they must process extensive data effectively, AI engineers must have proficiency in computing frameworks (e.g., Apache Spark)
- For optimum performance of Artificial Intelligence systems and optimal data flow, an AI engineer must have expertise in implementing and designing data pipelines and workflow automation.
- Advanced mathematics such as calculus, statistics, and algebra with a deep understanding of neural networks is also necessary.
Responsibilities
The responsibilities of an AI Researcher include:
- Researching and developing innovative models and algorithms.
- Collect and preprocess data, perform feature extraction, and manage noisy and missing data.
- Select an appropriate ML algorithm to predict and analyze data. Train and validate models using suitable datasets.
- Deploying trained models and integrating AI techniques into products and services
- Keep a beady eye on the deployed models and make required updates to improve accuracy.
- Address bugs or any other issues that might arise during operation.
AI Engineer’s Responsibilities include:
- Design, develop, and implement ML, AI, and Big Data systems. They are also involved in setting up Infrastructure and configuring computing environments, ensuring the smooth performance of AI models.
- Build prototypes, develop software systems, and test the performance of the systems.
- Integrate AI models into existing frameworks for continuous interface with other elements.
- AI engineers are involved in troubleshooting and system monitoring. They address problems such as data inconsistencies, system failures, etc., to ensure AI models’ optimal health.
- Stay informed about the latest trends in AI
Software and Tools
An AI Research Scientist must be familiar with the following:
- Programming languages – Python, Matlab and R
- Using Libraries such as Pandas and NumPy to clean, transform, and manipulate data.
- Experience in using research tools – Notebook, LaTeX, Jupyter
AI Engineer must be proficient in:
- C++, Python, Java
- AI libraries and frameworks such as PyTorch and TensorFlow
- Software tools – Docker, Jenkins, Git
- Cloud Platforms such as Microsoft Azure, Amazon Web
- AI, ML, and Big Data Frameworks – Cassandra, Hadoop and Kafka, Keras
Educational Background
AI Researchers
- Should be Ph. D holders in Mathematics, Statistics, Computer Science, or any other related field.
- Published papers in premier journals.
- Strong research background with experience in performing research.
AI Engineer must have:
- Bachelor’s or Master’s in Software Engineering, Computer Science, or any other related subject.
- Experience in building prototypes and developing software.
- Experience in AI, ML, and Big Data techniques.
Industries that Employ AI Researchers and AI Engineers
- AI Researchers find jobs in Academia, Large Technology companies, and Research Institutions.
- AI Engineers find employment in technology companies, consulting firms, and startups.
Key Differences between AI Researcher and AI Engineer
The AI landscape 2024 is a fusion of sophisticated advancements and ethical considerations. Here, we delineate the nuanced roles of AI Researchers and Engineers, highlighting their updated focuses and skills in the swiftly evolving domain of artificial intelligence.
| Aspect | AI Researcher | AI Engineer |
| Core Focus | Development of ethical AI practices, generative AI, enhancing AI with retrieval-augmented generation | Customization of AI models for specific applications, implementing MLOps, integration of AI in edge devices, privacy-preserving AI solutions |
| Emerging Skills | Understanding of AI policy, advanced data curation techniques, ethical AI frameworks | Specialization in efficient AI model architecture, data pipeline management, low-latency model deployment |
| Tools & Techniques | RAG, open AI models, Mixtral, LoRA, Quantization, and DPO for model optimization | Open-source AI models, advanced MLOps tools, AutoML, edge computing applications, quantization techniques |
| Methodologies | Focus on data strategy for generative AI, increasing transparency in AI decision-making, addressing bias in training data | Industrializing data science processes, managing data products and platforms, maintaining AI systems security |
| Innovation Areas | Advancing multimodal AI, fostering explainable AI, establishing robust data governance | Developing sophisticated virtual agents that can perform tasks autonomously, integrating AI with IoT |
| Ethical & Societal | Researching implications of AI in society, contributing to global AI ethics discourse | Implementing AI in compliance with evolving regulations, safeguarding against shadow AI risks, emphasizing diverse and unbiased AI development |
| Industry Evolution | Contributing to open AI model development and advocating for responsible AI adoption | Tailor AI solutions to industry-specific needs, adapt to GPU shortages and cloud costs, and leverage smaller, more powerful AI models. |
| Collaboration | Engaging with interdisciplinary teams to align AI developments with societal needs | Working closely with data scientists, product managers, and business stakeholders to deploy AI solutions that deliver practical value |
Conclusion
The key differences between AI researchers and AI engineers lie in their skills, primary focus, and responsibilities. While AI researchers design and build AI algorithms and models, AI engineers implement and deploy AI systems. There are significant differences in their educational backgrounds, responsibilities, skill sets, tools, and software used. Both these are critical roles, and there is a strong demand for them in the AI field. The employment for these roles is projected to grow substantially in the coming years.
FAQs
What does an AI Researcher do?
An AI researcher conducts research in the field of AI and builds cutting-edge technologies. They delve into complex issues and combine research methodologies and design concepts to create advanced algorithms that push the boundaries of machines, allowing them to learn, reason, and adapt.
How do I become AI researcher?
AI Researchers are from diverse backgrounds with a variety of specializations. The diversity in roles reflects the multitude of career trajectories, and each role contributes to the growth of artificial intelligence in unique ways. Becoming an AI Researcher is challenging, and one must be strong in computer science, mathematics, and domain-specific knowledge. AI researchers must have the ability to think creatively and abstractly, leading them to unexplored knowledge terrains.
What are the key areas of AI Research?
The main AI research fields include:
- Data Mining and Data Science
- Machine Learning
- Robotics
- Natural Language Processing
- Expert Systems
- AI Ethics and Fairness
- Cognitive Computing
- AI healthcare
- AI for Autonomous Vehicles.
What is the career progression of AI Researcher’s?
AI Researchers typically start their careers as junior Data scientists and Research Assistants and progress to AI researchers. With experience, they become Senior AI Researchers. Further advancement can lead to AI Research Manager, and top performers can reach positions such as VP of AI Research or Strategic Scientist.