Become a

LLM Engineer

Who Ships to Production

Go from API calls to production AI systems. Master LLM internals, RAG architectures, Knowledge Graphs, and agentic workflows — the skills that define the next generation of engineers.

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Core Curriculum

⚡LLM Engineering🔍RAG Systems🕸️Knowledge Graphs🤖AI Agents🎯Fine-Tuning⚙️MLOps
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Deep Articles

Free Knowledge Base

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Live Sessions

Per Cohort

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Learning Paths

Beginner → Advanced

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Hands-On

Real Projects

Why FDE@ProdAI

Built Different From Day One

Production-First

Deploy real systems, not toy demos

Live Cohorts

30 seats, real-time Q&A

52+ Free Articles

Full knowledge base, open forever

Real Projects

Production capstone deployments

Community

Discord, peer reviews, alumni

Structured Paths

No gaps, beginner to advanced

Programs

Structured Training for Production Engineers

Enrolling NowLIVE COHORT

LLM Engineering: From Tokens to Production

Transformer architectures, fine-tuning with LoRA, RAG systems, agent deployment, and production scaling.

Build production RAG systems
Fine-tune with LoRA/QLoRA
Deploy agentic AI systems
Scale inference in production
Starts: Aug 9, 2026View Details
3 Days

RAG in Production

Next: Sep 2026

5 Days

Knowledge Graphs

Next: Oct 2026

3 Days

AI Agent Dev

Next: Nov 2026

Curriculum

10 Weeks to Production Mastery

Week 1-2

LLM Foundations

  • Tokenization
  • Transformers
  • Attention
  • Positional Encoding
Week 3-4

Training & Fine-Tuning

  • Pre-training
  • LoRA/QLoRA
  • RLHF & DPO
  • Evaluation
Week 5-6

RAG & Retrieval

  • Embeddings
  • Vector Stores
  • Chunking
  • Re-ranking
Week 7-8

Knowledge & Graphs

  • Neo4j
  • RDF/OWL
  • SPARQL
  • Graph-RAG
Week 9-10

Agents & Production

  • Agent Arch
  • Tool Use
  • Multi-Agent
  • Deployment
Learning Path

From Zero to Production

Six phases, ten weeks. Each builds on the last.

Foundations

Transformer math, attention, tokenization

Core Techniques

LoRA fine-tuning, RLHF, embeddings

Build Systems

RAG pipelines, graphs, agents

Deploy

Scaling, monitoring, optimization

Evaluate

Red-teaming, bias, frameworks

Ship & Present

Capstone, peer review, demo

Knowledge Lab

52+ Deep Articles — Free

LLM

Transformer Architecture Deep-Dive

15 min

RAG

Building Production RAG Systems

20 min

Fine-Tuning

Fine-Tuning with LoRA & QLoRA

18 min

Ontology

Knowledge Graphs for AI

12 min

Agents

AI Agent Architectures

14 min

RAG

Vector Databases & Embeddings

10 min

Student Outcomes

Our Students' Growth

Real outcomes from working engineers who learned LLM internals and production AI systems

Got Salary Hike

₹4L → ₹24L6x
₹9L → ₹24L2.7x
₹13L → ₹46L3.5x
₹20L → ₹60L3x

Average salary increase after mastering LLM internals and production deployment

Notable Transitions

Software Engineer
Staff Engineer
Backend Developer
LLM Engineer
ML Engineer
AI Architect
Full Stack Dev
Principal Engineer

Engineers transitioning to senior AI roles at top companies

Impact & Reach

Built production RAG systems at scale
Published AI research papers
Led AI/ML teams at top companies
Created popular open-source AI tools

Students building production AI systems and contributing to the community

All outcomes verified — Students achieved these results through their efforts and our guidance

How To Join

Admission Process

1

Apply

Fill form with background & goals

2

Assessment

Short technical assessment

3

Interview

15-min cohort fit check

4

Enroll

Accept, pay, begin journey

Eligibility

Engineering degree or equivalent

1+ years programming (Python preferred)

Basic ML understanding

8-10 hours/week commitment