Python GenAI
Infosys Bengaluru East, Karnataka, India
Job Description
"Unlock the power of GenAI with Python at Infosys and propel your career forward in the rapidly evolving AI landscape."
As a Python GenAI expert at Infosys, you'll be at the forefront of developing and deploying cutting-edge AI solutions that transform businesses and industries.
With a strong foundation in Python programming, object-oriented programming (OOPs) concepts, data structures, and algorithms, you'll be equipped to tackle complex AI challenges and drive innovation.
Why you should learn this:
The demand for GenAI experts is skyrocketing, with a projected growth rate of 30% in the next 5 years, making it an ideal career choice for those in the AI and data science space.
Expected Salary: The average salary for a Python GenAI expert in India is ₹15-25 lakhs per annum, with experienced professionals earning up to ₹50 lakhs per annum.
How it works:
- Step 1: Develop a strong foundation in Python programming, including data structures, algorithms, and object-oriented programming (OOPs) concepts.
- Step 2: Gain hands-on experience with popular libraries such as NumPy, Pandas, Matplotlib/Seaborn, and Scikit-learn, as well as Agentic AI, Langchain, and Langgraph.
Core Concepts to Master
Agentic AI
Agentic AI refers to a type of AI that is capable of autonomous decision-making and action-taking. It involves the development of AI systems that can learn from experience, adapt to new situations, and interact with humans in a more natural and intuitive way.
Langchain
Langchain is a Python library that enables developers to build AI models that can reason, generate text, and interact with humans in a more natural way. It involves the use of techniques such as natural language processing (NLP) and machine learning (ML) to develop AI models that can understand and respond to human input.
MCP (Model-Data-Code-Platform)
MCP is a framework for building, deploying, and managing AI models. It involves the use of a combination of data, code, and platform to develop and deploy AI models that can be used in a variety of applications.
NumPy, Pandas, Matplotlib/Seaborn, and Scikit-learn
These libraries are essential for building and deploying AI models in Python. NumPy provides support for large, multi-dimensional arrays and matrices, while Pandas provides data structures and functions for efficiently handling structured data, including tabular data such as spreadsheets and SQL tables. Matplotlib and Seaborn are plotting libraries that enable developers to create high-quality visualizations of data, while Scikit-learn provides a wide range of algorithms for machine learning and data science tasks.
Interview Questions (Beginner)
- What is Python, and why is it used for AI development?
- What are the key differences between NumPy and Pandas?
- How do you handle missing data in a Pandas dataframe?
Job Overview
Advance Questions
- • Can you explain the concept of Agentic AI and how it differs from traditional AI?
- • How do you deploy a Langchain model in a production environment?
- • What are some best practices for integrating machine learning models with web applications or backend services?