Cloud and Solutions Architects are currently well positioned for future success. They know how to design solutions for scaling, security and reliability and possess the systems thinking foundations which most technology experts try to develop throughout many years of their career. However, the workload that is arriving on their roadmap is something that cannot be ignored anymore.
Generative AI is changing the requirements of enterprises regarding the people designing their architecture. Understanding how to build an architecture for predictable and consistent behaviour is a highly valuable skill that will only get you far if complemented with another one – designing an architecture for LLM-based systems, supporting autonomous AI agents, building a secure RAG pipeline, and implementing generative AI in production-ready state.
The problem is that most architects developed their skills before the emergence of generative AI. Infrastructure skills are well-developed, however, the unique design patterns of these new workloads are unknown to many experts yet.
This is exactly the reason for choosing the correct generative AI program by the Cloud and Solutions Architects who want to lead the direction of enterprise architecture development.
Why This Program Is Ideal for Cloud & Solutions Architects
This course material aligns directly with the capabilities of an architect, as most of what they do relate to system design, scalability, and dependability as opposed to greenfield development.
Designing infrastructure will apply directly to the compute-latency-cost considerations of LLM workloads
Integration of APIs and systems is a given at the start, and not something taught from the basics
Designing distributed systems will be directly applicable to architecting the multi-agent workflows involved
Security and access control experience will directly apply to the governance considerations of generative AI
Scale design experience will underpin the architecting of RAG pipeline and vector retrieval layer
What You'll Learn Throughout the Program
The curriculum adheres to a linear progression starting with basics and then going on to cover systems in production rather than systems that are just functioning in demonstration mode.
GenAI and LLMs foundations – transformers and foundation models
Advanced prompting and RAG – hybrid search and reranking
LLM fine-tuning and alignment using PEFT, LoRA, and QLoRA
Multimodal and Agentic AI – agents workflows with planning and tools
Deployment and optimization – latency, costs, and AI safety
Practical Learning Through Real Projects
This course will be centered on five deliverables rather than isolated assignments; therefore, each module results in something that works.
Enterprise RAG system with evaluation pipeline for the retrieval accuracy
Open-source LLM with fine-tuning using LoRA/QLoRA and deploying it as an API
Multi-agent system for multiple-step tasks execution
Containerized & monitored Generative AI API for real traffic handling
2-week capstone industry project with faculty supervision
Fees, Eligibility & Program Information
Here's what you're signing up for, cost and eligibility included:
Fee: ₹1,99,000 including taxes
Seat block: ₹10,000
EMI: starting from ₹6,825 per month
Qualifications for application: bachelor's degree with minimum 50% marks
Others: eligible if you have at least 2 years of experience in the technology domain
Skills needed: writing functions in Python, basic data structures, working with APIs, reading technical documentation, and familiarity with the basic mathematics and statistics behind common ML concepts
Certificate awarded: on campus at the time of graduation at IIT Kharagpur, handed over by the Programme Director and Institute leadership
Admission Process
Joining the program is straightforward and only requires three steps.
Fill in your details and complete your application.
Get shortlisted and receive your offer letter.
Block your seat.
Why Study Generative AI at IIT Kharagpur
IIT Kharagpur brings a few things to this program that are hard to find combined in a single online course.
India's first IIT, founded in 1951, with 75 years of educational experience
Ranked 5th in Engineering in NIRF 2025, among the top-ranked universities in India
Professors who regularly publish papers in conferences like NeurIPS, ICML, ICCV, ACL, and IEEE Transactions, so the syllabus is based on research and not tutorials
Classes conducted only by professors from the CSE department, with no third-party instructors involved at any point
Certificate awarded after an on-campus ceremony, not just a digital one
Conclusion
The Cloud and Solutions Architects are already ahead of most tech professionals in terms of building robust systems. By adding your skills in Generative AI and Agentic AI on top of this, you will not only keep up but place yourself in an even more coveted position as there are already organizations fighting to recruit people like you.
By pursuing IIT Kharagpur's EPGC in Generative AI and Agentic AI, you will have all the required knowledge, practical training, and certification to achieve this transition confidently. If you are interested in enterprise architecture and its future developments, you should definitely consider this program.
FAQs
How is designing Generative AI different from the infrastructure work architects already do?
The traditional model of designing the architecture relies on predictable requests and predictable scaling patterns. The generative AI-based systems generate unpredictable workloads in terms of computing requirements associated with the complexity of models used and their prompt length, as well as agent calling different tools while working on the task.
What specifically changes when an architect starts designing around LLM behavior instead of standard application logic?
Standard applications follow fixed logic paths that are easy to test and predict. LLM-based systems are less deterministic, so design shifts toward planning for variable latency, monitoring output quality in production, and building fallback paths for when a model or agent doesn't behave as expected. The program's RAG and multi-agent projects are built around exactly this kind of design work.
Can architects apply Generative AI skills directly to their current infrastructure role?
Yes, common applications include designing the hosting and scaling layer for internal AI tools, building secure RAG pipelines connected to company data, and architecting the guardrails around early agentic AI pilots. These use cases extend existing infrastructure work rather than replacing it.
What career roles can architects target after this program?
After completing the program, professionals can pursue roles such as AI Solutions Architect, Generative AI Architect, Agentic AI Systems Architect, and Cloud AI Platform Architect. These positions combine core architecture expertise with the ability to design, deploy, and manage enterprise-grade generative AI solutions. Compensation varies based on factors such as experience, industry, organization, and location, with AI-focused architecture roles generally offering competitive salary packages due to the growing demand for enterprise AI expertise.
Do architects need prior experience with LLMs before starting this program?
No, the curriculum is sequenced to start from GenAI foundations before moving into fine-tuning, RAG, and agentic systems. Python skills, basic data structures, and familiarity with APIs are enough to begin. Direct LLM experience is useful but not assumed going in.
How does Agentic AI extend what architects already know about distributed systems?
Agentic AI systems plan tasks, call external tools, and coordinate multi-step workflows instead of following a single fixed request path. For an architect, this is closer to designing a distributed system with dynamic routing than to a typical microservice call chain. The program's multi-agent project applies this directly to task execution scenarios.
Is this program only useful for senior architects, or can less experienced professionals benefit too?
The program assumes basic Python and API familiarity rather than years of experience specifically. Both early-career engineers moving into architecture and senior architects follow the same curriculum, though those with more hands-on infrastructure background may move through foundational sections faster.
How does a research-led faculty affect the quality of a Generative AI course for architects?
Faculty who actively publish at venues like NeurIPS, ICML, and ACL tend to teach concepts as they currently stand in research, rather than repackaging vendor tool tutorials. This matters in a field like generative AI, where techniques and best practices change quickly, and it means the course content is less likely to lag behind current architecture patterns.
Will learning Generative AI make an architect's existing infrastructure skills less relevant?
No, most real-world generative AI systems still depend on solid infrastructure design underneath the AI layer, whether that's the hosting environment, the data pipeline, or the security boundary. Generative AI skills add to that foundation rather than replacing it.
What makes the industry capstone useful for an architect's portfolio specifically?
A capstone that goes from a business problem to a deployed system demonstrates more than model-building skill, it shows the ability to handle infrastructure design, deployment, and monitoring end to end for an AI-specific workload. For architects, this fills a gap that many purely theoretical courses leave open, and it gives something concrete to walk through in interviews.