01
Generative AI & LLMs
Learn the fundamentals of LLMs, foundation models, model selection, inference concepts and common enterprise GenAI use cases.
Global Enrollment
Join from anywhere in the world
India
USA
UK
UAE
Saudi Arabia
Europe
Live Online
English
International Participants Welcome
8
Week Intensive Weekend Online Classes
Sep 27
Cohort Start Date
Global
Alumni Network
Scale the future
AI Architecture for Business Excellence
An elite fellowship program focusing on integrating scalable AI systems to drive business excellence, growth, and operational efficiency. Become an Enterprise AI Architect capable of designing production-ready AI systems from strategy and LLMs to RAG, agents, cloud, security and governance. 8-weeks live fellowship | 16 live sessions | 8 architecture modules | 1 enterprise capstone project | 1:1 coaching
Affiliations & Partnerships
NVIDIA
Microsoft
Google
IBM
Jotform
IASA Global
AEA
Applied AI
AI Sessions Conducted Globally In:
USA
•
UK
•
Dubai
•
Saudi Arabia
•
India
•
Bangladesh
Mastering distributed computing & clusters
Optimizing high-throughput AI inference
Bridging applied research & production
Connecting with elite tech visionaries
Official Credential
AI Fellowship Program Certification
Earn a prestigious certificate recognizing your successful completion of the AI Fellowship Program and your advanced knowledge in Enterprise AI Architecture, Generative AI, RAG, and AI Agents.
Showcase your skills and achievement as you build the expertise to design, govern, and scale impactful AI solutions for real-world business.
Prerequisites
Who Should Apply?
The fellowship is suitable for professionals with some prior exposure to technology, software, cloud, data, AI or business systems.
Recommended background:
- 3+ years of professional experience in technology, engineering, software, cloud, data, AI/ML, DevOps, architecture, consulting, product or related fields
- Basic understanding of software and technology concepts
- Familiarity with at least one area such as cloud, software development, data, AI/ML, DevOps or enterprise technology
- Basic understanding of how business or technical requirements are translated into technology solutions
- Motivation to develop Enterprise AI Architecture skills
You do not need:
- Advanced AI/ML research experience
- Prior experience as an AI Architect
- Deep mathematics
- Advanced programming expertise
- Previous experience building LLMs
- A computer science degree specifically
Know Your Coach
Ayisha Tabbassum
Learn directly from an industry leader shaping the future of AI Architecture. Ayisha brings unparalleled global expertise and a proven track record of excellence.
Free Download
Download the Full 8-Week Syllabus
Get a detailed week-by-week breakdown of the curriculum, capstone projects, and tools you'll master. See exactly what it takes to become an Enterprise AI Architect.
- 16 Live Session Details
- 8 Applied Assignments & Deliverables
- Enterprise Architecture Capstone Overview
8-Week Curriculum
Design, Build & Lead Enterprise AI Solutions.
8 Weeks
16 Live Sessions
8 Applied Assignments
1 Capstone
W1
AI Strategy & Architecture Foundations
AI transformation strategy, business use cases, AI maturity, architecture principles, enterprise AI roadmap
D1
Enterprise AI Strategy & Business Transformation
Key Topics Covered:
AI transformation, enterprise AI landscape, business use cases, AI maturity, AI opportunity identification, stakeholder analysis, AI value creation
Assignment / Practical:
Select a real business problem and identify 3–5 AI opportunities
Deliverable
AI Opportunity Assessment
D2
AI Architecture Foundations & Enterprise Roadmaps
Key Topics Covered:
AI architecture principles, architecture layers, AI capability map, reference architectures, business/technology alignment, enterprise AI roadmap
Assignment / Practical:
Prioritize AI opportunities and create a transformation roadmap
Deliverable
AI Transformation Strategy & Enterprise AI Roadmap
W2
Generative AI, LLMs, Prompt Engineering & RAG
LLMs, foundation models, model selection, multimodal AI, inference architecture, enterprise GenAI
D3
Generative AI & LLM Architecture
Key Topics Covered:
Generative AI, foundation models, LLMs, tokens, context windows, model capabilities, model selection, inference, fine-tuning concepts
Assignment / Practical:
Compare 3 LLM/model options for the selected business use case
Deliverable
LLM Selection Matrix
D4
Enterprise GenAI & Multimodal AI Architecture
Key Topics Covered:
Enterprise GenAI use cases, multimodal AI, inference architecture, APIs, scalability, latency, cost and deployment patterns
Assignment / Practical:
Design an enterprise GenAI solution
Deliverable
Enterprise GenAI Architecture Blueprint
W3
Prompt & Context Engineering
Advanced prompting, context engineering, structured outputs, evaluation, optimization and enterprise applications
D5
Advanced Prompt Engineering
Key Topics Covered:
Prompt frameworks, zero-shot, few-shot, role/task/context prompting, structured prompting, task decomposition, output control
Assignment / Practical:
Create and test multiple prompts for an enterprise use case
Deliverable
Enterprise Prompt Library
D6
Context Engineering, Evaluation & Optimization
Key Topics Covered:
Context windows, context selection, structured outputs, grounding, evaluation, hallucination mitigation, quality metrics, optimization
Assignment / Practical:
Build a context strategy and evaluate AI responses
Deliverable
Prompt & Context Engineering Playbook
W4
RAG & Enterprise Knowledge Architecture
RAG patterns, embeddings, vector databases, chunking, retrieval, hybrid search, RAG evaluation
D7
RAG Architecture & Knowledge Retrieval
Key Topics Covered:
RAG fundamentals, embeddings, document processing, chunking, indexing, vector databases, metadata, retrieval pipelines
Assignment / Practical:
Design a RAG solution for an enterprise knowledge use case
Deliverable
RAG Architecture Blueprint
D8
Advanced RAG & Enterprise Knowledge Architecture
Key Topics Covered:
Hybrid search, semantic search, reranking, retrieval optimization, knowledge architecture, RAG evaluation, access control, enterprise data integration
Assignment / Practical:
Design the complete enterprise knowledge architecture
Deliverable
Enterprise Knowledge Architecture
W5
AI Agents, Multi-Agent Systems & Automation
Agent architecture, tools, memory, orchestration, workflows, multi-agent systems and autonomous enterprise solutions
D9
AI Agent Architecture
Key Topics Covered:
AI agents, tools, function calling, memory, planning, workflows, reasoning patterns, human-in-the-loop
Assignment / Practical:
Select an enterprise process suitable for agent automation and design the agent
Deliverable
AI Agent Architecture
D10
Multi-Agent Systems & Enterprise Automation
Key Topics Covered:
Agent orchestration, multi-agent patterns, agent communication, workflow automation, autonomous systems, guardrails
Assignment / Practical:
Design a multi-agent enterprise workflow
Deliverable
Multi-Agent Enterprise Automation Blueprint
W6
AIOps, Observability & AI Operations
AIOps architecture, intelligent monitoring, event correlation, anomaly detection, incident prediction, automated remediation, AI observability, FinOps and SRE integration
D11
AIOps & Intelligent Operations
Key Topics Covered:
AIOps architecture, telemetry, metrics, logs, traces, event correlation, anomaly detection, incident prediction, intelligent alerting
Assignment / Practical:
Analyze an operational scenario and identify AI-driven operations opportunities
Deliverable
AIOps Architecture Blueprint
D12
AI Observability, FinOps & SRE
Key Topics Covered:
AI observability, LLM monitoring, reliability, SRE, FinOps, AI cost optimization, operational KPIs, automated remediation
Assignment / Practical:
Design monitoring, reliability, cost and remediation strategy
Deliverable
AIOps Implementation & Observability Guide
W7
Enterprise AI Cloud, Security & Governance
Cloud AI architecture, deployment, scalability, Kubernetes, CI/CD, security, identity, privacy, compliance, responsible AI and governance
D13
Enterprise AI Cloud & Platform Architecture
Key Topics Covered:
Cloud AI architecture, deployment models, scalability, Kubernetes, containers, CI/CD, MLOps/LLMOps, model serving, HA, disaster recovery
Assignment / Practical:
Convert an AI solution into a scalable cloud architecture
Deliverable
Enterprise AI Cloud Architecture
D14
AI Security, Identity, Privacy & Governance
Key Topics Covered:
AI security, identity, RBAC/ABAC, privacy, encryption, compliance, responsible AI, governance, guardrails, risk management
Assignment / Practical:
Conduct security and governance review of the architecture
Deliverable
Secure & Governed Enterprise AI Architecture
W8
AI Architecture Capstone, ROI & Executive Leadership
End-to-end architecture project, architecture review, business case, ROI, consulting strategy, executive presentation and career positioning
D15
Enterprise AI Architecture Capstone
Key Topics Covered:
Business requirements, architecture decisions, LLMs, RAG, agents, cloud, security, governance, observability, cost
Assignment / Practical:
Combine previous work into one end-to-end enterprise AI solution
Deliverable
Enterprise AI Architecture Capstone
D16
Architecture Review, ROI & Executive Leadership
Key Topics Covered:
Architecture review, trade-offs, TCO, ROI, business case, consulting strategy, executive storytelling, presentation, career positioning
Assignment / Practical:
Present and defend the solution to a simulated executive/architecture review board
Deliverable
Executive Architecture Presentation + Business Case & ROI
Tech Stack & Tools
Master the modern AI Architecture stack.
Understand how AI technologies, architecture patterns and enterprise considerations come together through structured learning and practical exercises.
02
RAG & Knowledge Systems
Understand embeddings, vector databases, chunking, retrieval and the main components of a Retrieval-Augmented Generation architecture.
03
AI Agents & Automation
Explore agent architecture, tools, memory, workflows, orchestration and practical enterprise automation scenarios.
04
Cloud, Operations & Governance
Learn the architectural considerations around cloud deployment, Kubernetes, CI/CD, observability, security, identity and AI governance.
Technologies & Concepts Covered
LLMs
Generative AI
RAG
Vector Databases
Prompt Engineering
AI Agents
LangChain
Cloud AI
Kubernetes
AIOps
Observability
LLMOps
MLOps
FinOps
AI Strategy & Use Cases
Explore how organizations identify AI opportunities, evaluate use cases and connect AI initiatives with business requirements.
Architecture Design
Practice creating architecture diagrams and making technology decisions for LLM, RAG and AI-agent use cases.
Practical Case Studies
Work through selected enterprise scenarios covering AI applications, operations, cloud, security and governance.
Why Choose Us
Not Just Another AI Course.
See how this fellowship goes beyond basic tutorials to build real architectural leadership.
| Criteria | Generic Online Courses | The Fellowship |
|---|---|---|
| Focus | Isolated models & tools (LangChain, OpenAI API) | End-to-end Enterprise AI Architecture & Strategy |
| Learning Approach | Pre-recorded videos & standard labs | Live sessions, architecture reviews & executive presentations |
| Outcome | Basic certificate of completion | Production-ready architecture portfolio & business case |
| Support | Community forums or TA support | Direct 1:1 coaching from a top 0.1% global AI Architect |
| Target Audience | Beginners & general developers | Senior engineers, tech leads & aspiring architects |
Admissions Process
A Selective Path to Enterprise AI Architecture.
A focused process designed to understand your experience, goals and readiness for the fellowship.
01
Check Prerequisites
Review the recommended background and confirm that the fellowship matches your current experience and learning goals.
02
Submit Your Application
Share your professional background, current role, technology experience, AI interests and goals for joining the fellowship.
03
Profile Review
Our admissions team reviews your application to understand your experience, motivation and alignment with the program.
04
1:1 Admissions Interview
Have a focused 1:1 conversation to discuss your background, expectations, AI interests and readiness for the fellowship.
05
Selection & Enrollment
Selected applicants receive an enrollment invitation with program details, payment information and next steps.
06
Welcome & Orientation
Complete enrollment, join the fellowship community and receive your learning resources and orientation information.
Career & Business Impact
Where AI Architecture Can Take You
Build expertise that can support higher-value career, leadership and business opportunities.
| Opportunity | Quantified Potential | What You Build | |
|---|---|---|---|
| Global AI Careers | $100K–$250K+ potential in select experienced AI Architect roles* | Enterprise AI, LLM, RAG, Cloud & Architecture expertise | |
| Career Growth | Promotion / expanded responsibilities | Architecture portfolio + executive communication | |
| Global Opportunities | USA • UK • Europe • Middle East • India | Globally relevant Enterprise AI capabilities | |
| AI Entrepreneurship | Multiple ₹1L+ consulting/project opportunities depending on market & expertise* | AI consulting, automation, products & SaaS concepts | |
| Multiple Income Streams | 5+ potential pathways | Employment • Consulting • Freelancing • Training • Advisory • AI Business |
Top 5 performers qualify for US Green Card EB1A profile building program.
Proven Outcomes
Career Impact & Job Placements
See how our graduates have transformed their careers, taking on high-impact AI leadership roles with significant salary growth.
Placed At
Global SaaS Enterprise
Before
Senior Software Engineer
After
Lead AI Architect
Salary Increase
+65%
Placed At
Top-Tier FinTech
Before
Cloud Solutions Architect
After
Head of AI Infrastructure
Average Post-CTC
₹1Cr+
Placed At
Global Consulting Firm
Before
Engineering Manager
After
VP of Enterprise AI
Career Growth
2.5x
AI Architecture Fellowship Stories
What Professionals Say About the Fellowship
Practical learning. Architecture thinking. Real-world application.
“The fellowship gave me a much clearer way to think about AI architecture. I particularly valued the connection between business requirements, LLMs, RAG, AI agents and cloud architecture. The architecture exercises helped me move beyond simply understanding AI concepts and start thinking about how these components fit into an enterprise solution.”
Pooja Menon
Digital Transformation Consultant
“The program helped me understand Enterprise AI from both a technology and business perspective. The sessions on AI strategy, LLMs, RAG and AI agents were especially valuable. I also appreciated the architecture frameworks and practical exercises, which gave me a more structured approach to evaluating AI solutions.”
Divya Krishnan
Product Manager, AI
“I joined the fellowship to strengthen my understanding of AI architecture beyond individual models and tools. The 8-week structure provided a logical progression from AI strategy and LLMs to RAG, agents, AIOps, cloud and governance. The assignments helped me apply the concepts to realistic enterprise scenarios.”
Sneha Iyer
AI/ML Engineer
“The combination of Generative AI, RAG, AI Agents, AIOps and Enterprise Architecture made the fellowship particularly relevant to my work. I found the focus on architecture decisions, business requirements and practical use cases very useful. It helped me develop a more structured way of approaching enterprise AI transformation.”
Rohan Malhotra
Technology Consultant
“The fellowship helped me connect my existing cloud architecture experience with emerging AI technologies. I particularly enjoyed working through enterprise architecture scenarios involving LLMs, RAG, AI agents and governance. The architecture templates and project-based assignments made the learning practical and easier to apply.”
Ananya Rao
Cloud Solutions Architect
“What stood out to me was the broader architecture perspective. Rather than focusing only on AI tools, the fellowship explored how AI solutions need to be designed around scalability, security, observability, governance and business value. The capstone was a useful way to bring the different concepts together.”
Vikram Shah
Engineering Manager, AI & Cloud
Participant experiences are shared with permission and reflect individual learning experiences.
FAQ
Questions about the program?
Who is this AI Architecture Fellowship designed for?
The fellowship is designed for AI/ML professionals, software and cloud engineers, solution and enterprise architects, technology leaders, engineering managers, consultants, product leaders, and entrepreneurs who want to develop practical expertise in Enterprise AI Architecture.
Do I need prior AI experience?
You do not need to be an AI researcher. However, a basic understanding of technology, software, cloud, data, or business systems will help you get the most from the program.
Are the sessions live or recorded?
The core fellowship sessions are conducted live online. Recordings are provided after the sessions so participants can revisit the material and reinforce their learning.
Is the program practical or mostly theoretical?
The fellowship is project-based and application-focused. Every week includes practical architecture work, case studies, exercises and an applied assignment that contributes to your AI Architecture portfolio.
What is the capstone project?
The capstone brings together the concepts learned throughout the fellowship. You will design an end-to-end enterprise AI solution covering business requirements, AI architecture, LLMs, RAG, agents, cloud, security, governance, observability, cost and ROI, followed by an executive-level presentation.
What resources are included?
Participants receive:
- 8-Volume Workbook
- Project Planner
- AI Architecture Design Templates
- AI Consulting Playbook
- AIOps Implementation Guide
- Practical Resources & References
- Session Recordings
- Career Coaching
- Fellowship Certification
- Community Access
Will I receive a certificate?
Yes. Participants who successfully complete the fellowship requirements receive an AI Fellowship Program Certification recognizing their learning and practical work across Enterprise AI Architecture and related domains.
Is career coaching included?
Yes. The fellowship includes 1:1 career coaching, providing guidance around AI Architecture career positioning, professional development and presenting your expertise effectively.
Will this help me become an AI Architect?
The fellowship is designed to develop the knowledge, architecture frameworks and practical portfolio needed to strengthen your AI Architecture capabilities. Career outcomes depend on your prior experience, application of the skills and individual circumstances.
Can working professionals join?
Yes. The fellowship is designed for working technology professionals and leaders. The online format allows participants to learn while continuing their professional commitments.
Can participants from outside India join?
Yes. The fellowship is delivered online and can accommodate participants from different countries. Session timings and payment options should be confirmed for international participants before enrollment.
Enrollment & Pricing
Apply for the Next Cohort.
Before Sept 20
₹1,00,000
After Sept 20
₹1,50,000
Payment to be made after selection in interview
Only 5 Seats Remaining
Join a community of elite engineers. Tentative start date is Sept 27th (actual dates announced once slots fill up).