How to become an AI Engineer in 2025?
- January 30, 2025
- AI & Data
- 14 min read
What is AI Engineering?
Artificial Intelligence Engineering focuses on designing, developing, and managing AI systems using various methods and tools. AI engineers create algorithms and advanced data processing techniques to solve complex problems, automate tasks, and optimize operations. They bridge the gap between theoretical Artificial intelligence and practical applications, ensuring systems are scalable, sustainable, and ethically sound.
To make this happen, AI engineers blend systems engineering, software development, computer science, and user-focused design. This mix helps them build smart systems that can tackle specific tasks or achieve set goals.
What Careers are Available to AI Engineers?
The AI Engineers field includes various career opportunities, which means you can take on different roles and responsibilities depending upon the position, experience and your interest. The most happening career for AI Engineers are as follows:
Machine Learning Engineer
| Why is it important in 2025 / Future? | Crucial for building intelligent systems and automating decision-making processes across industries; drives advancements in AI-driven automation and personalized experiences |
| Industries that Hire | Tech, Finance, Healthcare, Retail, Automotive |
| Skills Required for the Future | Advanced ML algorithms, Deep learning, Reinforcement learning, Cloud computing, Data engineering, Ethics in AI |
Data Scientist
| Why is it important in 2025 / Future? | Data-driven insights will be essential for decision-making; predictive analytics and data storytelling will drive competitive advantages in data-centric and customer-focused industries |
| Industries that Hire | Finance, Healthcare, E-commerce, Government |
| Skills Required for the Future | Advanced statistics, machine learning, data visualization, big data, cloud platforms, programming (Python, R) |
Robotic Engineer
| Why is it important in 2025 / Future? | Integral for developing automation in manufacturing, healthcare, and service industries; robotics innovation continues to streamline operations and increase efficiency. |
| Industries that Hire | Manufacturing, Automotive, Healthcare, Agriculture |
| Skills Required for the Future | Mechanical engineering, robotics programming, AI integration, control systems, ROS (Robot Operating System) |
Computer Vision Engineer
| Why is it important in 2025 / Future? | Vital for autonomous vehicles, surveillance, healthcare imaging, and augmented reality applications; enables visual data processing and enhances automation in vision-heavy tasks. |
| Industries that Hire | Automotive, Retail, Healthcare, Security, Agriculture |
| Skills Required for the Future | Deep learning, image processing, OpenCV, TensorFlow, 3D vision, embedded systems |
NLP Engineer
| Why is it important in 2025 / Future? | Essential for enhancing human-computer interaction, AI-driven language processing, and customer service automation; fuels growth in chatbots and voice-activated technologies. |
| Industries that Hire | Tech, Healthcare, Customer Service, Finance |
| Skills Required for the Future | Linguistics, deep learning, transformer models, text generation, sentiment analysis, conversational AI frameworks |
AI Product Manager
| Why is it important in 2025 / Future? | Critical for guiding AI solutions from ideation to deployment; bridges technical and business strategies to ensure AI-driven products meet market needs and regulatory standards. |
| Industries that Hire | Tech, Finance, Healthcare, Manufacturing, Retail |
| Skills Required for the Future | Product lifecycle management, data science, user experience design, regulatory understanding, strategic planning |
Roles and Responsibilities of an AI Engineer
AI Engineers are involved in the end-to-end creation and deployment of ML models. They convert complex-data into AI-driven solutions which will perform autonomously in real-time environments.
This role includes writing codes, deploying models to production, and continuously monitoring and updating them as needed. So let’s look at this more deeply!
- Programming - Writing code to build and operate AI systems effectively.
- Data Management - Organizing and handling data essential for AI model training and analysis.
- Data Science - Analyzing data to extract insights and create predictive models.
- Focused Machine Learning Research and Analysis - Conducting in-depth research to optimize machine learning techniques and models.
- Machine Learning - Applying algorithms that enable systems to learn from data and improve over time.
- Natural Language Processing - Teaching machines to understand and generate human language.
- Digital Image Processing - Processing visual data for analysis, pattern recognition, and AI-driven applications.
- Developing and Training Machine Learning Models - Building models that learn patterns from data for prediction and classification.
- Learn from Data - Continuously refining models by analyzing and learning from data trends.
- Building Data Science Infrastructure - Establishing systems that support data storage, processing, and analysis for AI.
- Creating Data Transformation Infrastructure - Setting up pipelines to clean, organize, and prepare data for analysis.
- Automate Processes by Utilizing Machine Learning - Streamlining tasks through intelligent, machine-learning-driven automation.
- AI Designer - Crafting user-centered AI experiences and ensuring the usability of AI systems.
- Productionization of AI Networks - Deploying AI models into scalable, reliable production environments.
- Software Design - Architecting software that supports AI functionality and user requirements.
- Deploy Software in Production - Making AI applications available for use in real-world environments.
- Implement Models - Applying trained models to solve specific business or technical problems.
- Test and Deploy Models - Ensuring models perform accurately before launching them in production.
- Testing and Evaluating AI Systems - Assessing AI systems for effectiveness, accuracy, and reliability.
- Staying Updated with AI Advancements - Keeping current with the latest trends and improvements in AI.
- Answer Business Challenges Using AI Software - Solving real-world business issues through AI solutions.
- Develop and Maintain AI Strategies - Planning and updating strategies for successful AI adoption and use.
- Coordinate with Other Team Members - Collaborating with cross-functional teams to integrate AI solutions smoothly.
How to Become an Artificial Intelligence Engineer?
The AI Engineer career path not only offers required financial rewards but also presents opportunities to work on innovative projects. If you want to become one of the profound Artificial Intelligence Engineer here are the steps that you need to follow:
- Complete a Bachelor's Degree
- Pursue a Postgraduate Degree
- Earn Certifications
- Acquire Practical Experience
- Apply for jobs
Complete a Bachelor's Degree
The expected areas of expertise required while graduating B.Sc or B.Tech are computer science, data science, mathematics, and information technology. As AI Tools has become popular in recent years, certain colleges have started to provide bachelor programs on AI targeted education in Artificial Intelligence concepts, machine learning and neural networks.Pursue a Postgraduate Degree
Those who have completed a bachelor's degree in science or technology can apply to AI engineering master's programs through online or offline training to upskill in the required field. Pursuing a Postgraduate is just an option - but it's not a must.Earn Certifications
If you are recently graduated or a working professional in the same field. It’s advised to get certified once in 2 years to upskill to the current trends and practices. Some of the certifications that are advised for beginners and advanced professionals such as AI+ Engineer.Acquire Practical Experience
Just acquiring knowledge and an educational degree doesn't make you an AI engineer. But, practicing it with real world applications, actively participating in events like Hackathon, and gaining experience using different tools and technologies used in the field of AI engineering.Apply for Jobs
Consider applying for a competitive AI Engineering related job post that is available on job portals. Understand the ways and needs that a company is chiseling you to produce. Face the consequences that have a critical impact on the Artificial engineering tools. And yes, by achieving all the above one can become a professional AI Engineer.As you have got the grip on how to become an AI engineer, it is very much essential to understand the skills that are needed to ace!
Top Skills Required to Become an AI Engineer
Technical Skills
There aren't many technical skills that you need to ace, but some of the important skills are as follows:- Data Modeling and Engineering - Mastering database design and ETL processes to ensure efficient data handling and integration.
- Big Data Analysis - Proficiency in using big data tools like Hadoop and Spark to analyze and derive insights from large datasets.
- AI Deployment and DevOps - Implementing CI/CD pipelines and using containerization tools like Docker and Kubernetes for seamless AI model deployment.
- AI Security - Understanding and applying security measures to protect AI systems and data from threats and vulnerabilities, an AI Security course equips you to protect AI systems effectively.
- Advanced Mathematics - Using advanced math like linear algebra, calculus, and probability to create and improve AI algorithms.
- Algorithm Knowledge - Building a solid understanding of core algorithms and their use in addressing intricate issues.
- Neural Network Architecture - Designing and optimizing neural network architectures using frameworks like TensorFlow and PyTorch for various AI applications.
Non Technical Skills
Knowing only some important skills cannot make you influence the juniors and seniors around. It needs following intricate skills to show your prominence among the group:- Communication and Collaboration - AI engineers need to effectively communicate complex ideas and work collaboratively with cross-functional teams to achieve project goals.
- Adaptability - They must be flexible and open to learning new technologies and methodologies as the field of AI rapidly evolves.
- Enthusiasm towards Continuous Learning - A passion for continuous learning is essential to stay updated with the latest advancements and innovations in AI.
- Team Management - AI engineers often lead projects and teams, requiring strong leadership and organizational skills to manage tasks and guide team members effectively.
Annual Salary and Job Outlook for AI Engineer
Recent studies by pwc mentioned that the AI impact on businesses will contribute $15.7 trillion to the global economy by 2030 [2]. From the past two-three years Artificial Intelligence industry has exponentially grown and undoubtedly led to increase in job opportunities and higher salaries for AI engineers in third world countries.
With artificial intelligence many web apps have been created over categories like virtual assistants, chatbots, music generation and many more. The potential for artificial intelligence to revolutionize many industries makes it no surprise that companies are willing to pay top dollar for engineers with this skill set.
As per the recent reports of ZipRecruiter (November 2024), the average salary for an AI engineer in the United States of America is approximately $106,386 a year as of September 2024. However the salaries can vary by locations. From the above related discussions, the job outlook for AI professionals looks promising [3].
As per the recent articles of University of San Diego, mentioned:
U.S. Bureau of Labor Statistics, employment for computer and information technology occupations, including AI roles, is projected to grow faster than the other occupations from 2023 to 2032.
Here is the list of job roles and experiences to be, to earn the top dollar, as the recent research by Glassdoor [4]:| Job Role | Required Experience | Salary Range per Year |
| Machine Learning Engineer | 2 to 4 Years | $132K - $212K |
| Lead Machine Learning Engineer | 5 to 7 Years | $185K - $278K |
| Machine Learning Engineer Manager | 5 to 7 Years | $191K - $286K |
| Prinicpal Machine Learning Engineer | 8+ Years | $192K - $299K |
| Senior Prinicipal Machine Learning Engineer | 10+ Years | $219K - $351K |
| Senior Manager of Machine Learning | 5 to 7 Years | $250K - $376K |
| Vice President of Machine Learning | 8+ Years | $229K - $428K |
| Director of Machine Learning | 8+ Years | $250K - $415K |
Understanding the financial rewards of an AI Engineer role is essential for aspiring professionals. Recent insights into AI Engineer Salary highlight not only the competitive pay but also the factors influencing earnings across industries and roles.
What Courses and Certifications are Available to AI Engineers?
The programs are intended to provide professionals the skills and information they need to succeed in the quickly changing digital world of today, with an emphasis on real-world application and industrial relevance are as follows:
The AI courses offered by NetCom Learning including the AI Engineer course is designed to provide participants with a comprehensive understanding of artificial intelligence technologies and their applications. The course covers essential topics such as machine learning, deep learning, natural language processing, and computer vision.
After completing the above course, you will gain the required knowledge and skills to earn the AI+ Engineer certification.
Tools and software Used by AI Engineers
Is AI Engineer same as ML Engineer?
For your clarification, Machine Learning comes under Artificial Intelligence. So AI engineers design, develop and implement solutions that enable the machines to supplement and augment human intelligence and abilities and ML engineers focus on developing the machine learning algorithms that will allow computers to make decisions based on the data sets.
AI engineers work is rooted in computer science and extended to applications that are needed for various industries such as chatbots in customer service to predictive analytics in finance. ML engineers create models that analyze the vast amount of data and make predictions or automate decision-making based on the data.
What is the Future of AI Engineering in 2025?
We are in just the beginning of revealing the capabilities of Artificial Intelligence. The future of AI is characterized by rapid growth and innovation. As the companies increasingly integrate AI into their operations, the demand for skilled AI professionals will increase drastically. It offers a variety of career paths with lucrative salaries and significant economic contributions. The dynamic nature of this environment will require professionals to continually learn and adapt.
