The rapid expansion of artificial intelligence across industries has created a fundamental shift in how software, data, and decision systems are built. However, most engineering graduates are still trained to treat AI as isolated model-building exercises, without understanding how these models behave once deployed in real, constrained, and continuously evolving environments. This program exists to address that gap by developing engineers who can build AI systems that function reliably in production on a scale.

To achieve this, the program is structured around the idea that true AI engineering requires more than algorithms, it requires mastery over data systems, distributed computation, model behavior under real-world constraints, and end-to-end deployment pipelines. Students are progressively trained through a tightly integrated pathway that begins with mathematical and computational foundations and advances into machine learning systems, deep learning architectures, and generative AI models that operate at industrial scale.

Learning is reinforced through continuous exposure to real infrastructure environments, including high-performance GPU systems, enabling students to work with large-scale models rather than simplified academic simulations. Alongside technical depth, the program emphasizes system thinking, where students learn to design AI solutions that balance accuracy, scalability, latency, cost, and ethical responsibility.

By the end of the program, learners are prepared not only to develop AI models, but to engineer complete AI systems that can be deployed, monitored, and improved in real-world production ecosystems across industries.

Key Highlights of the Program

• Access to industrial-scale GPU infrastructure (NVIDIA DGX H200-class systems)
• Training in billion-parameter model development and fine-tuning
• End-to-end AI lifecycle: data engineering, modeling, deployment, monitoring
• Hands-on experience with NLP, Computer Vision, and Generative AI
• MLOps, distributed systems, and scalable AI engineering
• Exposure to LLMs, transformers, and multimodal AI systems
• Embedded responsible AI, ethics, and governance framework
• Industry-aligned capstone projects and research exposure

Career Pathways

Program Structure

Eligibility

• Passed 10+2 or equivalent from a recognized Board / Council with a minimum of 50% marks (45% for SC/ST) in aggregate, and Physics & Mathematics as compulsory along with one of the subjects - Chemistry / Biotechnology / Biology / Computer Science.
• Valid score in JEE (Main / Advanced) or AUET (Alliance University QUASAR Entrance Test) or Karnataka state-level entrance examinations.

Duration

Four years, full-time (eight semesters), including elective capstone projects, design studies, internships, research interpretation.

The field of robotics is undergoing a fundamental shift from pre-programmed industrial automation to intelligent, adaptive systems capable of perception, reasoning, and autonomous action in unstructured environments. This program is designed to prepare engineers who can operate at this intersection of machine intelligence and physical systems engineering.

It focuses on the idea that true robotics expertise cannot be developed through simulation alone but must emerge from direct engagement with real-world robotic systems that integrate sensing, control, computation, and actuation. Students are trained to understand how machines perceive their environment through sensors, how they interpret uncertainty, and how they translate decisions into precise physical movement.

The learning journey progresses from mathematical and computational foundations into robot kinematics, control systems, perception, and reinforcement learning, ultimately culminating in the design and deployment of fully autonomous systems. These systems are tested not only in virtual environments but also on physical robotic platforms such as mobile robots, manipulators, and drones.

By the end of the program, students are capable of building end-to-end autonomous systems that integrate AI-driven perception, decision-making, and real-time control for real-world applications in industry, mobility, healthcare, and defense.

Key Highlights of the Program

• Full-stack robotics development from hardware integration to AI decision systems
• Hands-on deployment on physical robotic platforms including drones, manipulators, and mobile robots
• Strong focus on ROS, SLAM, computer vision, and motion planning systems
• Integration of reinforcement learning for adaptive robotic behavior
• Real-world exposure through industry-linked robotics projects
• Interdisciplinary engineering across mechanical, electronic, and software systems
• Focus on real-time control, perception uncertainty, and system reliability
• Application-driven learning in manufacturing, mobility, and autonomous systems

Career Pathways

Program Structure

Eligibility

• Passed 10+2 or equivalent from a recognized Board / Council with a minimum of 50% marks (45% for SC/ST) in aggregate, and Physics & Mathematics as compulsory along with one of the subjects - Chemistry / Biotechnology / Biology / Computer Science.
• Valid score in JEE (Main / Advanced) or AUET (Alliance University QUASAR Entrance Test) or Karnataka state-level entrance examinations.

Duration

Four years, full-time (eight semesters), including elective capstone projects, design studies, internships, research interpretation.

Modern industrial and societal systems are increasingly defined by the tight integration of computation, physical processes, and human interaction. This program is designed for engineers who will build and govern these interconnected systems where software decisions directly influence physical outcomes in real time.

Cyber-Physical Systems operate at the intersection of embedded computing, real-time control, sensing, communication networks, and intelligent decision-making. In such environments, system failures are no longer purely digital—they manifest physically across manufacturing lines, transportation systems, energy grids, healthcare devices, and autonomous infrastructure.

This program prepares engineers to design systems that are continuously aware, adaptive, and resilient. Students learn how to model physical processes digitally, simulate real-world environments through digital twins, and deploy intelligent control systems that operate under strict timing, safety, and reliability constraints.

The learning pathway progresses from foundational engineering and computation into embedded systems, real-time operating systems, industrial IoT, and finally into large-scale cyber-physical ecosystems that include collaborative robotics, smart infrastructure, and human-integrated systems such as wearable and biomedical networks.

By the end of the program, graduates are capable of designing and managing Industry 5.0 systems that unify computation, hardware, and human-centric intelligence in real operational environments.

Key Highlights of the Program

• Integrated design of computation, physical systems, and human interaction layers
• Hands-on development of real-time embedded and industrial control systems
• Digital twin modeling and simulation of complex industrial environments
• Exposure to Industrial IoT systems including sensors, PLCs, SCADA, and edge networks
• Training in CPS security, resilience, and fault-tolerant system design
• Collaborative robotics and adaptive production system engineering
• Introduction to Internet of Bodies (IoB) and wearable biomedical systems
• Deployment of full-stack intelligent systems in real-world industrial environments

Career Pathways

Program Structure

Eligibility

• Passed 10+2 or equivalent from a recognized Board / Council with a minimum of 50% marks (45% for SC/ST) in aggregate, and Physics & Mathematics as compulsory along with one of the subjects - Chemistry / Biotechnology / Biology / Computer Science.
• Valid score in JEE (Main / Advanced) or AUET (Alliance University QUASAR Entrance Test) or Karnataka state-level entrance examinations.

Duration

Four years, full-time (eight semesters), including elective capstone projects, design studies, internships, research interpretation.