Advanced Route: Production Ai Engineering
Course Fee
₹13000.00
(inclusive of GST)
Course Overview
8 Months
Start Date
July 12, 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
Transformer Architecture & Tokenization Foundations
- 1Embeddings: From Discrete to Continuous Space
- 2The Attention Mechanism
- 3Self Attention, Multihead Attention, Masked Multihead Attention
- 4Positional Encoding
- 5Encoder-Decoder, Encoder-Only, Decoder-Only Transformers
- 6Cross-attention
- 7Taxonomy of Tokenization (Word, Subword, Character, Byte level)
- 8Byte Pair Encoding (BPE)
- 9WordPiece
- 10SentencePiece
Learn from Industry Experts
Why This Course
Go from LLM Basics to Production Engineer
Move from foundational transformer mechanics and tokenization to building complete, autonomous LLM systems. Master the transition from base models to post-training workflows that are useful for real-world production. Experience a structured path from advanced Python programming to deep hands-on implementation.
Master the Advanced LLM Engineering Stack
Move beyond prompting to master high-scale techniques like QLORA, SFT, and DPO for model alignment. Gain expertise in advanced retrieval architectures, including Matryoshka embeddings and Multi-vector retrieval. Learn to standardize tool integration with MCP (Model Context Protocol) and cross-provider function calling.
Build an Enterprise-Ready Portfolio
Graduated with five comprehensive, end-to-end projects, including a domain-specific healthcare LLM and a legal RAG system. Demonstrate mastery over complex agentic systems and multi-agent peer delegation. Showcase skills in knowledge distillation and model compression for cost-effective CPU inference.
Learn Production-Ready Deployment & LLMOps
Understand the lifecycle of shipping production-grade models using vLLM, SGLang, and Docker. Build eval-gated CI/CD pipelines that enforce regression, cost, and performance gates. Deploy to cloud infrastructure like AWS SageMaker and ECS with full tracing and observability.
Engineer the Future: Agents & Orchestration
Transition from simple chat interfaces to stateful workflows using LangGraph's graph-based architecture. Implement human-in-the-loop checkpoints and state persistence to ensure safe, supervised autonomy. Orchestrate systems with A2A (Agent-to-Agent) protocol for complex task discovery and delegation.
Build Safe, Reliable AI (Evaluation + Guardrails)
Secure AI systems with PII masking, prompt injection defence, and policy-based access control. Use advanced evaluation frameworks like RAGAS, PromptFoo, and LLM-as-judge to ensure faithfulness and relevance. Master context compression and pruning to manage cost and latency without sacrificing intelligence.
Skills You Will Acquire
Frequently Asked Questions
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