If you are planning to build a career in AI, understanding the role, skills, career opportunities, and growth path of an AI Engineer can help you make an informed choice.
An AI engineer builds, tests, and deploys practical artificial intelligence applications by integrating pre-trained foundation models, APIs, and machine learning techniques into production-grade systems
A career in AI Engineering requires technical and practical skills, including Python, AI tools, data management, model development, deployment, and hands-on projects.
AI Engineers can explore opportunities across industries such as technology, finance, healthcare, e-commerce, manufacturing, education, and automotive, with opportunities to progress into senior and leadership roles.
This Guide Covers What an AI Engineer does, including key roles, responsibilities, skills, and tools. AI Engineer salary in India, career scope, industries, and growth opportunities. How to start a career in AI Engineering and the skills needed to build a successful career.
What Is an AI Engineer and What Do They Do?
An AI Engineer is a professional who develops AI software to help solve problems and make processes easier for users. AI engineers design AI-driven software, applications, and tools that can learn from data, process information, make predictions, and create content.
The techniques used by AI engineers include machine learning, deep learning, NLP, computer vision, and generative AI among others. They also guarantee the usability of AI software in practical applications.For those looking to build a strong foundation in computer applications and technology, pursuing an Online MCA can also be a useful step towards an AI-focused career.
Key Roles and Responsibilities:
The main responsibilities of an AI Engineer include:
AI systems development: Creating AI models and applications to address a certain issue.
Data processing: Gathering and preparing data for the AI model.
AI training: Educating the AI model to identify patterns, predict outcomes, or perform certain actions.
Development of AI applications: Implementing AI in the website, application, chatbot, or software.
Management of generative AI: Creating AI applications which generate various types of content such as texts, pictures, code, etc.
Testing AI systems: Verifying the accuracy of the results generated by the AI system.
Improving AI models: Identifying issues and improving the AI models.
Deployment of AI solutions: Giving the user access to AI applications.
Cooperation with other departments: Collaborating with the software engineers, data scientists, and other business units.
Why This Role Matters in Business Today:
Companies are applying AI in order to save time, automate processes, learn about their customers, and make smarter decisions. The role of AI Engineer involves turning AI technologies into practical applications that would benefit the business.
The examples of the tasks which an AI Engineer might deal with include creating chatbots, recommendation engines, and other software designed to automate employees' work.
In connection with the growing use of generative AI and automation within companies, the demand for specialists capable of developing and managing AI-based solutions is also increasing. AI Engineers can work in a variety of industries including technology, banking, health care, retail, education, manufacturing, and e-commerce.
Skills and Tools Every AI Engineer Needs
An AI Engineer needs a mix of technical skills, programming knowledge, and practical AI skills to build and manage AI systems. They also use different tools and platforms to develop, test, and deploy AI applications.
Core Technical Skills
Some of the important skills an AI Engineer should have include:
Programming: Programming knowledge of languages like Python, Java or C++ is valuable for AI app creation and AI model work.
Machine Learning: Machine learning knowledge allows engineers to create systems that learn and predict data.
Deep Learning: Neural network knowledge is useful for working with more complex AI applications, including image recognition and language processing.
Data Management: AI engineers should have skills related to collecting, cleaning, organizing and analysing data for training purposes.
Natural Language Processing: Natural Language Processing knowledge is required to build applications that deal with natural human language.
Generative AI: LLMs, prompt engineering and AI model integration knowledge become increasingly valuable for building Generative AI applications.
Cloud and Deployment: Knowledge of cloud and deployment technologies allows AI engineers to deploy their applications for the customers.
Problem-Solving: Problem-solving abilities allow AI engineers to identify problems in AI systems and solve them.
For students looking to build a strong foundation in AI, machine learning, and computer applications, an Online MCA in Artificial Intelligence and Machine Learning can help develop relevant technical skills. The programme can also provide exposure to AI concepts and practical applications that support technology-focused career paths.
Popular AI Tools
AI Engineers have different tools depending on the kind of projects they deal with. Examples of some common tools that they can use include:
TensorFlow - a framework for machine learning used for training and building AI models.
PyTorch - a framework for machine learning and deep learning projects.
Scikit-learn - a Python library that is commonly used for machine learning, classification prediction, and data analysis.
Hugging Face - a platform which offers AI models, datasets, and tools for natural language and other AI projects.
OpenAI APIs - the tools used by developers to add generative AI and language-model capabilities to their applications.
Google Cloud AI - cloud-based tools used for building and deploying AI solutions.
Microsoft Azure AI - AI solutions and tools for building and deploying AI applications.
Jupyter Notebook - an environment where code is written, data is analysed, and machine learning models are tested.
Having the right blend of skills and tools enables AI engineers to design effective and reliable AI solutions for business.
AI Engineer vs Data Scientist vs Machine Learning Engineer
While all these three positions have something to do with artificial intelligence and data, they differ significantly in terms of responsibilities. Thus, the AI Engineer concentrates on developing applications based on artificial intelligence, the Data Scientist is involved in searching for useful insights using data, and the Machine Learning Engineer concentrates on developing and deploying ML models.
In other words, an AI engineer deals with the development of AI-based solutions; a data scientist works on data analysis; and finally, a machine learning engineer deals with the implementation of machine learning models.
AI Engineer Salary in India
Salary of an AI Engineer in India is based on some considerations such as experience, skill, location, employer, and responsibilities. As stated by Glassdoor, the current base salary for AI Engineers in India is about ₹9 lakh per annum, while the base-pay salary range is about ₹6 lakh to ₹15 lakh per annum.
Salary by Experience Level
AI Engineers' salaries can be higher if professionals have gained experience and developed advanced technical skills. Salaries as per recent salary data on Glassdoor can also differ significantly based on experience and location.
(glassdoor)
These figures are based on recent Glassdoor salary submissions and can vary by location, employer, and individual profile.
Factors That Affect AI Engineer Salary
There are several factors that can influence the salary of an AI Engineer in India:
Experience: Higher experience levels will be beneficial for professionals to get into more advanced AI jobs.
Technical skills: Technical skills like Python, machine learning, deep learning, generative AI, and LLMs are relevant for an AI Engineer job profile.
Specialization: Having knowledge about specialized domains like NLP, computer vision, machine learning, or generative AI can influence one's salary and job offer.
City: There can be differences in salaries based on cities. Recent salaries on Glassdoor have shown the differences in salaries for Bengaluru, Mumbai, Chennai, and Delhi.
Company: Salary packages are highly different based on the company. This is evident from the different AI Engineer salary ranges listed by Glassdoor for companies like IBM, TCS, Accenture, and Deloitte.
Responsibilities: If the role involves advanced AI development, model deployment, or project management in AI, salaries could vary accordingly.
Experience: AI projects, internships, and practical experience with AI software can help candidates acquire relevant skills for the role.
(Source: Glassdoor)
Career Scope and Growth Path
Being an AI Engineer is a promising profession. Various organizations from multiple industries employ artificial intelligence to automate processes and develop more intelligent software and applications. AI engineers can specialize in diverse domains and advance their careers up the ladder with time.
Industries Hiring AI Engineers
There are various fields in which one can work as an AI Engineer, including:
Technology: Developing AI-infused software, applications, chatbots, and other digital tools.
Banking and Finance: Building systems for identifying frauds, risk assessment, customer service, and financial prediction.
Healthcare: Handling data related to health care, healthcare applications, and AI-powered support systems.
E-commerce and Retail: Building recommendation systems, customer service chatbots, and demand prediction tools.
Manufacturing: Utilizing AI for conducting quality assurance, automation, maintenance prediction, and production planning.
Education: Building personalized learning systems, AI tutors, and automated exams.
Automotive: Working on computer vision, driver assistance technology, and other AI technologies.
Consulting: Helping businesses identify applications of AI and building AI-powered solutions for businesses.
How to Start a Career in AI Engineering:
To enter into a career as an AI Engineer, take these steps:
Build up a foundation: study basic mathematics, statistics, programming, and computer science.
Study Python: begin by studying Python, as it is frequently used in AI and machine learning.
Study AI and machine learning: learn about machine learning, deep learning, natural language processing, computer vision, and generative AI.
Get familiar with AI tools: become acquainted with tools such as PyTorch, TensorFlow, Scikit-learn, and Hugging Face.
Do projects: create projects that include chatbots, recommendation systems, predictive models, and AI assistants.
Gain experience: internships, freelancing, or junior level positions can give you practical experience.
Make a portfolio: showcase your AI projects and code in your portfolio, which you can build using GitHub.
Look for AI positions: apply for jobs like AI Engineer, Machine Learning Engineer, AI Developer, and Generative AI Engineer.
With experience, an AI Engineer can progress from entry-level positions to Senior AI Engineer, AI Architect, Lead AI Engineer, or AI/ML Manager roles.
Is AI Engineering the Right Career for You?
AI Engineering is a promising career for those who love technology, coding, problem solving, and handling data. However, there will always be learning involved since the technologies used by AI continue to evolve.
Who Should Consider an AI Engineer Career:
An AI Engineering career may suit you if you:
Enjoy coding and working with technology.
Are interested in artificial intelligence and machine learning.
Like solving problems and finding simple solutions.
Enjoy working with data, patterns, and algorithms.
Are interested in areas such as Generative AI, NLP, computer vision, or robotics.
Are comfortable learning new tools and technologies.
Want to build AI-powered applications and products.
Enjoy working on projects that combine coding and AI.
What to Know Before Choosing This Career
Here are some factors that you should consider before pursuing AI Engineering as a career:
Technical expertise is important: You'll learn programming, machine learning, data handling, and other technical concepts.
There is continuous learning involved since AI evolves constantly, thus requiring you to learn new techniques.
Hands-on projects will provide experience outside of theoretical lessons on how AI works.
It is challenging: Developing and refining AI systems will involve troubleshooting.
Every role requires different expertise, such as AI Engineer, Machine Learning Engineer, Data Scientist, and Generative AI Engineer.
Industry-specific knowledge will make your technical knowledge even more valuable.
Build Your AI Engineering Career with DY Patil Online
DY Patil Online provides the necessary tools of acquiring knowledge and skills to discover various career opportunities within the field of AI and technology.
Gain technical skills: acquire basic skills in programming, data, artificial intelligence, and other technology concepts.
Study online: engage in the process of studying through a convenient online medium.
Acquire practical knowledge: engage in practical project-based learning and activities related to application of technology in real-world situations.
Acquire industry-specific skills: develop knowledge in data analytics, artificial intelligence, and other advanced technological concepts.
Study while juggling other responsibilities: the convenience of online courses makes them perfect for both students and professionals.
Lay foundations for future technological careers: establish a base for future career opportunities in AI, data, software, and technology domains.
Conclusion
AI engineering is an approach that involves programming, machine learning, data, and the use of modern technologies like Generative AI to create functional AI solutions. Applications of AI engineering include chatbots, recommendation engines, automation tools, forecast apps, and others.
Entering a career in this area requires technical skills, experience with projects, and continuous learning due to constant development in the field of AI technology. With the proper skills, tools, and practical experience, individuals may explore career options in such industries as technology, finance, health care, e-commerce, manufacturing, and others.
