Dr. Natarajan sir, I am working as junior developer in Bangalore IT company for 18 months after completing B.Tech from VTU. I very worry that AI will replace developers like me who doing basic coding. I want to grow into AI field but don't know where to start. Should I learn Python and machine learning by myself or take formal course? How can someone from small engineering college compete with IIT graduates who get all good AI jobs? What specific skills I should focus for make myself AI-ready and not become obsolete in next 5 years?

Ans: What is your age and qualification?

Ans: Pursuing a career in Artificial Intelligence (AI) is a great decision, as the field has immense growth potential and can transform various industries. AI is reshaping sectors like healthcare, finance, automotive and retail, making it an extremely sought-after skill in today's job market. According to industry reports, the global AI market is projected to grow at a compound annual growth rate (CAGR) of over 30%, with the demand for AI professionals expected to grow by more than 40% in the next decade.

With AI expanding rapidly, the demand for skilled professionals is growing faster than the supply. Roles like AI engineer, machine learning engineer, data scientist and NLP specialist are not only in high demand across industries, they are also offering competitive salaries and advancement opportunities. These roles are crucial for building intelligent systems that improve business efficiency, enhance user experience and solve complex problems.

As an AI professional, you'll have the chance to work on cutting-edge technologies such as natural language processing (for creating chatbots and virtual assistants), computer vision (for image recognition and autonomous vehicles), and deep learning models (for voice recognition and predictive analytics). The skills you develop in this field will enable you to contribute to groundbreaking innovations and have a positive impact on society.

The career path typically begins with building a strong foundation in programming, especially Python, and mathematics, including linear algebra and statistics. Once you've mastered these basics, you can explore specializations such as machine learning, deep learning, and computer vision or NLP. Continuous learning is essential because AI is constantly evolving, so you'll need to stay updated on the latest research and techniques.

The future scope of AI is vast, covering sectors such as healthcare (personalised medicine, predictive diagnostics), finance (fraud detection, algorithmic trading) and manufacturing (automation, smart systems). Additionally, if you are interested in developing an AI-powered product or startup, AI offers ample opportunities for entrepreneurship.

Job growth in AI is fueled by the increasing adoption of AI technologies in small and large business operations and everyday applications. Companies are leveraging AI for automation, predictive analytics, customer insights, and intelligent systems. Key industries such as healthcare use AI for predictive diagnostics and personalized treatment plans, while finance relies on AI for fraud detection and algorithmic trading. In manufacturing, AI drives automation and smart systems, significantly enhancing efficiency and productivity.

Ultimately, a career in AI not only offers future-secure employment but also the chance to contribute to the transformational technologies that define the modern world. It is a field where constant innovation means there is always something new to learn and create, providing long-term growth and satisfaction.

Ans: Yes you should be conversant with AI. Traditional coding and testing jobs will be eliminated.

Ans: Here’s a simple step-by-step path you can follow to get started in AI:
1. Start with Python – Since you have a technical background, learning Python will be easy. It’s the main language used in AI. Use beginner-friendly platforms like Coursera or Udemy.
2. Learn AI Basics – Begin with the basics of machine learning (ML), which is a core part of AI. A good course is Andrew Ng’s ML course on Coursera.
3. Explore Practical Tools – Learn tools like TensorFlow or PyTorch (for ML models) and basic data handling using Pandas and NumPy.
4. Focus on Projects – Try small hands-on projects like predictive maintenance (can relate to your industry), image recognition, or control systems using AI. This will bridge your domain knowledge with AI.
5. Join AI Forums or Groups – Connect with AI communities, maybe even ones for professionals in engineering fields. You’ll learn a lot from discussion and real problems.
6. Optional: Certifications – If you want formal recognition, go for AI certifications from IBM, Google, or Microsoft.
Since you’re already a consultant, you could eventually apply AI to industrial automation, fault detection, or predictive analytics in cement and power sectors.
Go step by step—don’t rush. Even 1 hour a day will help you grow steadily.