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M.Tech in Artificial Intelligence
This specialization focuses on core areas of modern AI, including deep learning, natural language processing (NLP), computer vision, and reinforcement learning. Students gain expertise in developing sophisticated algorithms and models that enable machines to perceive, reason, and act intelligently, preparing them for cutting-edge roles in research and industry.
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Career path
Certifications
Eligibility
Programs
Quick Links
FAQs
Placement Records
Our M.Tech in Artificial Intelligence is built for specialists and researchers exploring advanced ML architectures, probabilistic reasoning and AI ethics through rigorous coursework, research projects and industry collaborations. We focus on developing professionals who design and deploy intelligent solutions to complex real-world problems.
93%
Thesis Completion Rate
90%
Advanced Certification Rate
2x
Industry-Sponsored Research Projects
3x
Internship Conversions
The different career paths available for the program
AI Research Scientist
MLOps Engineer
Generative AI Engineer
Computer Vision Specialist
NLP / Conversational AI Engineer
AI Ethics & Governance Analyst
AI Product Manager and many more
AI Research Scientist
MLOps Engineer
Generative AI Engineer
Computer Vision Specialist
NLP / Conversational AI Engineer
AI Ethics & Governance Analyst
AI Product Manager and many more
ServiceNow CSA & CAD certifications build advanced skills in AI-driven IT service management and enterprise workflow automation, preparing students for senior cloud solution roles.
AWS ML Specialty certification validates expert-level skills in building and deploying scalable ML pipelines, bridging postgraduate academics with production-grade cloud engineering.
Azure AI-102 equips students with specialised skills in Cognitive Services and Azure OpenAI, preparing them for senior AI engineering roles in enterprise environments.
Academic Requirement
Candidates must have passed a 4-year Bachelor’s Degree in Engineering/Technology in the relevant branch from a recognized University.
Candidates must have secured a minimum of 50% aggregate marks in the qualifying undergraduate examination.
Entrance Test Requirement
Admission shall be based on merit through:
Graduate Aptitude Test in Engineering (GATE)
TS PGECET
Anurag Common Entrance Test (Anurag CET)
Qualifying Examination on merit
For Foreign / NRI category, admission shall be based on merit in GATE or marks secured in the qualifying examination.
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From collaborative spaces to student communities, explore what shapes the Anurag experience.
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Beyond classroom learning
Learning that extends beyond lectures and coursework.
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Research Overview
Discover research areas, facilities, and opportunities to innovate.
What does the program focus on?
It focuses on advanced concepts in artificial intelligence, machine learning, and intelligent systems.
Is the program research-oriented?
Yes, it includes research-based learning along with practical implementation.
Are hands-on projects included?
Yes, students work on real-world AI models and applications.
What prerequisites are helpful for this program?
Basic knowledge of programming, mathematics, and data handling is beneficial.
How is the curriculum structured?
It combines theoretical foundations, lab work, and research-driven projects.
