AI Systems Architect Program
Build real-world AI systems with LLMs, RAG, AI Agents, evaluation, and production architecture through hands-on projects and live mentorship.
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Class scheduleTue, Thu, Sat · 8:00 AM - 9:30 AM IST
Tue, Thu, Sat · 8:00 AM - 9:30 AM ISTMeets
2026-11-03Starts
2027-01-07Ends
20Seats
10Modules
5Projects
₹35,399Incl. GST
Enrol · ₹35,399 incl. GST
20 seats left
Why learn live, with a cohort
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Live, mentor-led sessionsLearn in real time with Sivaranjan A and a batch of peers, not alone with videos. Your trainer shares the class recording if you miss one.🔨
Real projects you shipBuild 5 hands-on projects that go straight into your public portfolio, reviewed by your trainer.🎓
Certificate + visibilityFinish to unlock a verifiable certificate, 5 exam passes and 5 ByteLabs projects, and learn in public to get noticed.What you will learn and build
The full curriculum, module by module, with hands-on project steps along the way.
1
AI Foundations & Modern AI
Learn how AI has evolved from Machine Learning and Deep Learning to Generative AI and modern LLM-based systems. Understand how AI models work at a practical level, where different AI approaches fit, how to identify real-world AI opportunities, and how to approach problems from an AI system-design perspective. By the end, you will be able to break down a business problem and identify where AI can actually add value.
2
LLM Application Engineering
Learn to build practical LLM-powered applications using model APIs, prompt engineering, structured outputs, context management, streaming, function calling, tool use, and multi-model workflows. Understand how to manage model failures, inconsistent outputs, and application-level errors. By the end, you will be able to design and build reliable LLM applications instead of simply writing prompts.
Project
Build Your First AI Application
Build a complete AI application that solves a real-world problem. You will define the problem, design the AI workflow, integrate an LLM, connect tools or external services, handle failures, and turn the idea into a working application. You will finish with a demonstrable project that can be added to your GitHub and portfolio.
3
RAG & Knowledge Systems
Learn how to connect LLMs with private and domain-specific knowledge using Retrieval-Augmented Generation. Understand document ingestion, preprocessing, chunking strategies, embeddings, retrieval, context construction, prompt design, and grounded generation. By the end, you will be able to design a RAG pipeline that allows an AI system to answer questions using your own data rather than relying only on the model's internal knowledge.
4
Vector Databases & Intelligent Retrieval
Understand how modern AI systems store and retrieve knowledge using vector databases. Learn embeddings, similarity search, indexing, metadata filtering, hybrid retrieval, reranking, and retrieval optimization. Explore tools such as FAISS, Qdrant, Chroma, and other vector database approaches. By the end, you will be able to choose and implement an appropriate retrieval strategy based on accuracy, performance, scalability, and use case requirements.
Project
Build a Production-Style RAG System
Build a complete document intelligence assistant from the ground up. You will ingest and process documents, generate embeddings, create a searchable knowledge base, retrieve relevant information, construct context, generate grounded answers, and expose the system through an application. You will also test retrieval quality and improve the system based on measurable results.
5
AI Evaluation & Reliability
Learn how to measure whether an AI system actually works instead of relying only on subjective output quality. Understand retrieval precision, recall, relevance, groundedness, faithfulness, evaluation datasets, latency, regression testing, and failure analysis. By the end, you will be able to create an evaluation strategy and identify exactly where an AI system is performing poorly and how to improve it.
6
AI Agents & Tool Use
Understand how AI agents differ from traditional chatbots and build systems capable of reasoning through tasks, selecting tools, retrieving information, executing actions, maintaining context, and handling failures. Learn agent workflows, tool calling, planning, memory, human-in-the-loop patterns, and multi-step task execution. By the end, you will be able to design an agent capable of completing meaningful tasks rather than simply generating text.
Project
Build an Autonomous AI Agent
Build an AI agent capable of understanding a goal, planning a workflow, selecting appropriate tools, retrieving information, executing multiple steps, handling failures, and producing a final result. You will test the agent's behavior, identify failure points, improve its workflow, and finish with a working autonomous AI project for your portfolio.
7
AI Systems Architecture
Learn how to architect reliable, scalable, and production-ready AI systems. Design model routing, primary and fallback models, rate limiting, retries, timeouts, circuit breakers, caching, observability, evaluation pipelines, and failure-handling strategies. Understand how architecture decisions affect cost, latency, reliability, scalability, and user experience. By the end, you will be able to take an AI prototype and design a production-grade system around it.
8
Production AI & AI Products
Learn how to transform an AI prototype into a real product. Work with backend APIs, databases, authentication, application architecture, Docker, deployment, logging, monitoring, security, cost management, and scalability. Understand the engineering considerations involved in taking an AI application from your local machine toward production.
Project
Build & Deploy an AI Product
Build a complete AI-powered product combining LLMs, RAG or agents, backend APIs, databases, application logic, and a user interface. You will take the product from idea to architecture, implementation, deployment, and testing. You will also apply production practices for reliability, monitoring, security, performance, and cost management.
9
AI Product Strategy & Enterprise Use Cases
Learn how to identify AI opportunities that solve actual business problems. Translate business requirements into technical requirements, evaluate AI feasibility, select appropriate models and architectures, define success metrics, estimate complexity, and understand the trade-offs between different approaches. By the end, you will be able to think about AI not just as technology, but as a product and business capability.
10
AI Architect Capstone
Learn how to approach a complex AI problem from an architect's perspective. Define requirements, design system architecture, select models and infrastructure, plan data and retrieval flows, establish evaluation strategies, and address reliability, scalability, security, latency, and cost. You will learn how to defend your architectural decisions and communicate them clearly.
Project
Final AI Systems Capstone
Build a complete portfolio-ready AI system from problem discovery to architecture, implementation, evaluation, deployment, and presentation. You will receive mentor feedback on your architecture, technical implementation, AI quality, reliability, scalability, and business value. By the end, you will have a substantial project that you can demonstrate to employers, clients, or potential collaborators.
Your trainer
S
Sivaranjan A
Leads this live cohort on CareerByteCode: sessions, project reviews, and your path to the certificate.
Common questions
Is this live or recorded?
Live, scheduled sessions with your trainer and cohort. Your trainer adds the class recording, files and notes to the classroom afterwards, so you can catch up if you miss one.
What do I get at the end?
Finish the cohort to unlock a verifiable CareerByteCode certificate, 5 exam passes and 5 ByteLabs projects, plus your shipped project work in a public portfolio.
How is the price shown?
The price is the base plus 18 percent GST, shown all-in (₹35,399). You pay securely via Razorpay.
Do I need experience?
Pick a cohort at your level (Intermediate here). Your trainer supports you through every module and project.