Software Developer
Thinkatic Pune/Pimpri-Chinchwad Area
Job Description
"Unlock the potential of developing cutting-edge AI solutions at Thinkatic, where innovation meets collaboration."
As a Software Developer at Thinkatic, you'll be part of a dynamic team that's revolutionizing the way we interact with AI. With a strong focus on innovation and customer satisfaction, we're looking for talented individuals to join our ranks and help shape the future of AI development.
From conceptualization to deployment, you'll be working on real-world projects that push the boundaries of what's possible with AI. Our team is passionate about creating something epic, and we're looking for like-minded individuals to join us on this journey.
Why you should learn this:
The demand for skilled AI developers is on the rise, with a projected growth rate of 20% in the next 5 years.
Expected Salary: The average salary for a Software Developer at Thinkatic is between ₹8-15 Lakhs per annum, depending on experience.
How it works:
- Conceptualize and design AI solutions that meet customer needs and expectations.
- Collaborate with cross-functional teams to develop and deploy AI-powered projects.
Core Concepts to Master
Machine Learning Fundamentals
Gain a deep understanding of machine learning concepts, including supervised and unsupervised learning, neural networks, and deep learning.
Natural Language Processing (NLP)
Learn to develop AI models that can understand, generate, and process human language, with a focus on NLP techniques and applications.
Cloud Computing and DevOps
Understand the principles of cloud computing and DevOps, including containerization, orchestration, and continuous integration and delivery.
Interview Questions (Beginner)
- What is machine learning, and how is it used in AI development?
- Can you explain the difference between supervised and unsupervised learning?
- How do you approach debugging and troubleshooting AI models?
Job Overview
Advance Questions
- • Design a neural network architecture to solve a real-world problem.
- • Implement a natural language processing pipeline using popular libraries and frameworks.
- • Explain the concept of explainability in AI and its importance in decision-making.