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.
Core Curriculum
Deep Articles
Free forever
Live Sessions
Per cohort
Hands-On Labs
Real deployments
Alumni Rating
Verified outcomes
Salary Growth
Avg after program
Seats / Cohort
Small, focused group
Built Different From Day One
Most AI courses teach you to run notebooks. We train you to own the full stack — from architecture to production metrics.
Production-First
Deploy across all three major cloud AI stacks — GCP (Vertex AI, Cloud Run, GKE), AWS (Bedrock, AgentCore), Azure (OpenAI, Foundry, AI Search). Real infra. Zero toy demos.
Live Cohorts Only
30 engineers per cohort. Real-time Q&A, pair programming sessions, and instructor office hours every week.
Production-Grade Articles
52+ deep-dives with working code, architecture diagrams, and battle-tested patterns. LLM internals, RAG, Agents, MLOps — engineer-level, not tutorial-level.
Capstone Deployments
Graduate with a portfolio of 3+ live production systems: a RAG API, a fine-tuned LLM, and an agentic pipeline.
Structured Paths
Five clear tracks — Bootcamp → Engineer → Architect. No prerequisite gaps, no syllabus bloat, just a clean learning arc.
Structured Training for Production Engineers
10 Weeks to Production Mastery
Core AI Engineer track. Extended programs go deeper into each module.
LLM Foundations
- Tokenization & BPE
- Transformer architecture
- Attention mechanisms
- Positional encoding
RAG & Retrieval
- Dense & sparse embeddings
- Vector stores (Qdrant, Pinecone)
- Chunking strategies
- Re-ranking & HyDE
Fine-Tuning
- LoRA & QLoRA from scratch
- RLHF & DPO alignment
- Dataset curation & quality
- RAGAS evaluation
Knowledge Graphs
- Neo4j schema design
- RDF / OWL ontologies
- SPARQL queries
- Graph-RAG pipelines
Agents & Production
- ReAct & LangGraph agents
- Tool use & MCP
- Multi-agent coordination
- Cloud deployment + CI/CD
From Zero to Production
Six phases. Each builds directly on the last.
Foundations
Transformer math, attention, tokenization — understand how LLMs actually work, not just how to call the API.
Core Techniques
LoRA fine-tuning, RLHF, embeddings, vector search — the building blocks every production AI engineer needs.
Build Systems
RAG pipelines, knowledge graphs, multi-agent orchestration — architect and code complete production systems.
Deploy
Containerize, Kubernetes, Vertex AI, Cloud Run — ship your systems to real infrastructure with CI/CD.
Evaluate
Red-teaming, RAGAS, bias audits, cost dashboards — measure quality and safety the way top teams do.
Ship & Present
Capstone demo day, peer code reviews, alumni feedback — graduate with a portfolio, not a certificate.
Three Steps to Getting In
No lengthy applications. We care about motivation and foundational skills — not prestige.
Apply
Fill out a short form with your background, goals, and the program you're targeting. No entrance exam.
Assessment
A 30-minute async technical screen — Python, basic ML concepts. We review fit, not perfection.
Enroll & Begin
Accept your offer, complete payment, get access to pre-work. Cohort kickoff on day one.
Eligibility
Engineering degree or equivalent experience
1+ years programming (Python preferred)
Basic understanding of ML concepts
8–10 hours / week commitment
Limited seats · Sep 2026 cohort
Ready to build AI that ships?
Join engineers from Google, Amazon, Flipkart, and top AI startups. Applications close when the cohort fills.