Advanced Route-Production AI And LLM Engineering-Frontier AI From Research To Production
Course Fee
₹13000.00
(inclusive of GST)
Course Overview
8 Months
Start Date
July 19, 2026
Sat & Sun 8:00 to 11:00 Pm IST
Launch Your Data Career
This 8-month, industry-focused program is designed for experienced practitioners who want to move beyond foundational theory and master the architecture of high-scale, real-world AI systems. This is not an introductory course; it is a deep dive into the complete modern LLM stack—from transformer core mechanics and advanced fine-tuning to multi-agent orchestration and production-grade LLMOps.
By the end of this journey, you will not just be building prototypes, you will be shipping enterprise-ready LLM systems that reason, retrieve, and collaborate autonomously. You will master the deployment of complex architectures, including **Graph RAG portals** with Neo4j, vision-language models, and multi-agent DevOps pipelines** integrated with human-in-the-loop safety gates and automated CI/CD rollbacks on AWS.
Course Curriculum
Transformers 101
- 1Embeddings : From Discrete to Continous Space
- 2The Attention Mechanism
- 3Self Attention
- 4Multihead Attention
- 5Masked Multihead Attention
- 6Positional Encoding
- 7Encoder–Decoder Transformers
- 8Encoder-Only Transformers
- 9Decoder-Only Transformers
- 10Cross-attention
Learn from Industry Experts
Why This Course
Architecture
The only course covering the complete LLM stack From transformer math and tokenisation internals to fine-tuning, RAG, agents, and production deployment — every layer covered in one cohesive program. No course-hopping required.
Fine-tuning
Every fine-tuning paradigm, in one place LoRA, QLoRA, DoRA, AdaLoRA, SFT, DPO, GRPO, ORPO — master all of them with working code. You'll understand the math, the trade-offs, and exactly when to use each technique.
RAG Systems
Eight RAG architectures — from vanilla to production Vanilla, Hybrid, Multimodal, Graph, Agentic, Vectorless, Corrective, and Adaptive RAG. Each pattern is taught end-to-end with evaluation, caching, and security layers included.
Agents
Build agents that actually ship to production LangGraph state machines, PydanticAI type-safe agents, MCP servers, and A2A protocols — the full 2026 agentic stack. From ReACT loops to multi-agent supervisor-worker systems.
Multimodal
See, hear, and reason — full multimodal coverage Vision Transformers, CLIP, SigLIP, DINOv2, VLMs, and Whisper speech fine-tuning. Most LLM courses skip this. Here it's a first-class module with hands-on notebooks.
Reasoning
Reasoning models and MoE — properly explained DeepSeek-R1-Zero, GRPO, Chain-of-Thought training, and Mixture-of-Experts architecture in full depth. Understand exactly how frontier reasoning models are built — not just how they behave.
AI Security
Security is a module, not a footnote Prompt injection defense, PII masking with Presidio, jailbreak prevention, RBAC with JWT, LLM gateways, and Bedrock Guardrails. Real enterprise security built into the curriculum from day one.
Production
Context and harness engineering for reliable agents Context window architecture, memory systems, LLMingua compression, eval harnesses with Inspect AI, and agent checkpointing with LangGraph. The skills that separate prototypes from production systems.
Tooling
Industry tooling, not toy examples Unsloth, Axolotl, LLaMA-Factory, vLLM, SGLang, LangSmith, Logfire, Langfuse, Argilla, distilabel. You graduate knowing the real stack, not just the theory behind it.
Synthetic Data
Synthetic data pipelines — the leverage point Self-Instruct, Alpaca-style generation, LLM-as-Judge scoring, preference dataset creation, and model collapse prevention. Build high-quality training data without a data collection team.
Efficiency
Small language models and knowledge distillation Student-teacher paradigms, hard vs. soft labels, KL divergence loss, attention transfer, and SLM design philosophy. Learn to build models that run on edge hardware without sacrificing quality.
Protocols
MCP and A2A — the protocols defining 2026 agents Build MCP servers and clients from scratch. Implement A2A-compliant agent discovery and delegation. These emerging standards are already deployed in production — and now you'll know them cold.
Enterprise
Enterprise patterns: RBAC, gateways, and multi-tenancy Role-based access control with SAML and JWT, LLM gateways with LiteLLM and Bedrock, retrieval-layer isolation, and data partitioning. The architecture slides enterprises actually need.
Architecture
Scaling laws and architecture trade-offs, fully explained Chinchilla, compute-optimal training, KV cache memory math, attention variants (MHA, MQA, GQA, MLA), and PagedAttention. Make architectural decisions grounded in numbers, not intuition.
Skills You Will Acquire
Frequently Asked Questions
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