Live Online Training
Advanced Python for LLM & Multi Agent Systems Course
Move beyond LLM basics. Build production-grade LLM applications, RAG systems, and multi-agent architectures in Python. Master async, structured outputs, LangChain, LangGraph, and the engineering patterns behind real AI products. For engineers ready to work at the frontier.
Live Online Training
Production Engineering
LangChain & LangGraph
Industry Certificate
Trusted by Learners & Professionals from Top Companies
WHY LEARN LLM ENGINEERING TODAY?
Build What Every AI Company Now Ships
- LLM engineering is one of the highest-compensated technical roles in the US market
- Every AI-native company is hiring engineers who can ship, not just prototype
- Move from LLM API basics to production-grade multi-agent architectures
- Async, structured outputs, RAG, vector DBs, and agent frameworks in one program
- Requires working Python — this is not an introductory course
WHAT YOU'LL LEARN
Advanced Python for AI
Async Programming (asyncio)
LLM API Integration
Structured Outputs & Function Calling
RAG System Design
Vector Databases
Agent Frameworks (LangChain / LangGraph)
Production Engineering & Testing
TOOLS & FRAMEWORKS YOU'LL MASTER
Python 3.12
Modern typed Python for production AI
FastAPI
The async API framework of choice for LLM apps
LangChain
Widely adopted LLM application framework
LangGraph
Stateful graph-based agent orchestration
OpenAI API
The GPT family through code
Anthropic API
Claude models for reasoning-heavy tasks
Pinecone
Managed vector database at scale
Weaviate
Open-source vector DB with hybrid search
Chroma
Local vector DB for prototyping
Hugging Face
Open-weight models and inference
Pydantic
Structured data and validation
Poetry
Modern Python dependency management
COURSE CURRICULUM
01
02
03
04
05
Week 1
Advanced Python for AI
- Type hints and Pydantic
- Modern packaging with Poetry
- Testing patterns for LLM code
- Decorators and context managers
- Logging and observability
Week 3
Async & I/O for LLM Apps
- Asyncio fundamentals
- Concurrent API calls
- Streaming responses
- Rate limiting and retries
- Error handling for stochastic outputs
Week 5
LLM APIs & Structured Outputs
- OpenAI and Anthropic APIs
- Function calling in production
- Pydantic-based structured outputs
- Cost engineering
- Caching and batching
Week 8
RAG Systems & Vector Databases
- Embedding models
- Chunking strategies
- Pinecone, Weaviate, Chroma
- Hybrid search and re-ranking
- Retrieval evaluation
Week 11
Multi-Agent Orchestration
- Single-agent ReAct loops
- LangGraph state machines
- Supervisor-worker patterns
- Tool design for agents
- Multi-agent debate and verification
What You Will Learn
The Twelve-Week Curriculum
The curriculum is organized into four progressive modules across twelve weeks. Each module ends with a practical project that integrates the techniques from that block, and the course as a whole culminates in a full multi-agent system as the capstone.
You will Explore
Type Hints & Pydantic
asyncio & Concurrency
Testing Stochastic Systems
Modern Python Tooling
Module 2 moves from advanced Python into direct work with foundation models through their APIs. Topics include the OpenAI, Anthropic, and Google APIs; structured outputs and function calling as the backbone of reliable LLM applications; prompt engineering for application developers (distinct from conversational prompting); caching, batching, and cost management; evaluation and quality control; and streaming for real-time user experiences.
The module ends with a project: a working LLM-powered application with structured outputs, error handling, caching, and basic evaluation.
You will Explore
Multi-Provider LLM APIs
Function Calling & Structured Outputs
Cost & Latency Engineering
LLM Evaluation Basics
Module 3 focuses on RAG, the architectural pattern behind most production LLM applications that work with custom data. Topics include embeddings and semantic search; hands-on work with Pinecone, Weaviate, Chroma, and pgvector; document processing pipelines and chunking strategies; retrieval evaluation independent from generation quality; and advanced RAG patterns including multi-step retrieval, query rewriting, and re-ranking.
The module ends with a working RAG system built end-to-end against a real document corpus.
You will Explore
Embeddings & Semantic Search
Vector Databases Deep Dive
Chunking & Document Pipelines
Advanced RAG Patterns
The final module covers the architectural frontier of LLM applications. Topics include hands-on work with LangChain and LangGraph; tool use and tool design; multi-agent orchestration patterns (supervisor-worker, peer-to-peer, debate and verification); state management in agent systems; monitoring and debugging agents; and practical safety and guardrail patterns for keeping autonomous agents within bounds.
LangChain & LangGraph
Tool Design for Agents
Multi-Agent Orchestration
Safety & Guardrails
The final two weeks are dedicated to the capstone: a complete multi-agent system built by each learner, scoped to a real problem, presented in the final session.
Capstone Project
Architect • Build • Deploy
By the end of twelve weeks, you'll have shipped a real LLM application worth showing in interviews at US AI companies.
REAL-WORLD PROJECTS
Async LLM Wrapper Library
Build a production-grade Python wrapper with retries, caching, and streaming.
Domain RAG Assistant
End-to-end retrieval system on your chosen document corpus.
Structured Output Extractor
Turn unstructured documents into validated data.
Multi-Agent Research Assistant
Coordinated agents for research and synthesis.
Custom Agent with Tool Library
An agent with domain-specific tools you design and build.
Course Format and Delivery
01
Schedule
- Each session runs approximately 90 minutes.
02
Recordings
Every session recorded and available within 24 hours.
- Access anytime, anywhere.
03
Cohort Interaction
Small class sizes ensure every learner gets direct instructor interaction. Cohort discussion channel active throughout.
- Learn together. Ship together.
04
Live Coding
Every session is a hands-on build. Learners write production Python during class, not just watch demonstrations.
- Practice. Apply. Get Feedback.
05
Code Reviews
- Ship-quality feedback.
Prerequisites and Technical Requirements
Working Python proficiency — functions, classes, dictionaries, standard library
Familiarity with Git, the command line, and basic API calls in code
A laptop with at least 16 GB of RAM and a modern OS
Plan for 9–13 hours per week (3 live sessions + independent practice + project work)
Why LLM Engineering Skills Matter In The Us Job Market
The Highest-Demand Technical Specialty in AI.
LLM engineering is currently one of the most quickly growing roles in US technology. Job titles that specifically require this skill set — AI engineer, LLM engineer, applied AI engineer, generative AI engineer — appear in employer listings faster than qualified candidates can fill them.
Compensation That Reflects the Scarcity.
Mid-to-senior LLM engineering roles at well-funded AI companies and large technology employers regularly reach into high six-figure total compensation, with the top end of the market reaching seven figures at frontier AI labs. Demonstrated production capability — not just theory — is what unlocks the higher end of these ranges.
From Prototyper to Ship-Ready Engineer.
Most self-taught LLM builders can prototype. Few can ship production systems that handle real user load, real edge cases, and real cost constraints. This course closes exactly that gap — the difference between a demo that impresses and a system that runs.
Learn From Industry Leaders
Michael Anderson
Lead Instructor — AI & Cloud
- AWS Certified
- 10+ Years Industry Experience
- Published AI Researcher
Senior AI engineer with extensive experience designing machine learning platforms and deploying enterprise AI solutions.
David Miller
Cloud & MLOps Instructor
- Cloud Architect
- Google Cloud Certified
- Enterprise MLOps Specialist
Cloud Architect specializing in scalable AI infrastructure, MLOps automation, and multi-cloud deployments for enterprise.
INDUSTRY RECOGNIZED CERTIFICATE
Receive an industry-recognized certificate after successful course completion.
- Industry Recognized
- LinkedIn Shareable
- Verifiable Certificate
- Career Boost
Career Impact
What Our Learners Say
The AI for Data Analytics course gave me the exact tools needed to interpret complex data streams quickly. The practical framework for integrating modern AI directly into existing analytical workflows was immediately applicable.
The multi-agent simulations in the Advanced Python for LLM & Multi Agent Systems course completely transformed our engineering team's approach to complex system design. Exceptional depth and real-world utility.
Enrolling our product team in Agentic AI & Multi Agent Autonomous Systems provided the strategic edge we were missing. The hands-on project structures made deploying autonomous agents far more straightforward.
After taking NLP with LLMs, I was able to fine-tune and deploy a custom retrieval-augmented model in weeks. The instructors cover production-grade deployment strategies that you rarely find in standard online courses.
The Executive Diploma in Deep AI & Cloud Intelligence offered a clear, high-level strategic overview without glossing over technical rigor. It helped our leadership team align our enterprise cloud architecture with modern AI solutions.
Bridging the gap between prototype models and production infrastructure can be tough, but MLOps & AI in Production laid out the best practices clearly. Highly recommended for any engineering group scaling AI services.
Ready To Ship Real LLM Systems?
Join a cohort of engineers building production-grade AI applications.
Frequently Asked Questions
The introductory Python for AI & Data Science course teaches Python from scratch with focus on NumPy, Pandas, and statistics. This course assumes you already have that foundation and focuses on the language features (async, typing, modern tooling) and ecosystem (LLM APIs, vector databases, agent frameworks) that AI engineering specifically requires.
Yes, and this is actually a common profile in the cohort. The course focuses on the engineering side of LLM work, not the ML theory side. Software engineers without an ML background often find this course more directly career-relevant than the classical ML track.
No. The course works entirely with LLM APIs, not with model training, so no GPU is required.
LangChain, LangGraph, and direct construction of agents using the OpenAI and Anthropic APIs. The course is deliberately balanced between framework-based and framework-free approaches because both patterns appear in real production codebases.
These are referenced and demonstrated where relevant. The course does not attempt comprehensive coverage of every framework because the landscape changes faster than any curriculum can keep up. The focus is on underlying concepts, with hands-on work in the frameworks that are most stable and widely used in production.
Yes, if you already have working Python proficiency from elsewhere. What matters is the skill level, however it was acquired.
The LLM ecosystem evolves quickly, but the underlying concepts — structured outputs, RAG architectures, agent patterns, async Python — have stabilized enough that the foundational material remains valuable. Specific frameworks and APIs are updated each cohort.
Most employers in this space evaluate candidates on demonstrated technical work — code samples, project repositories, technical discussions in interviews — rather than on credentials alone. The course is designed to produce work that holds up to that kind of evaluation.
Start Building at the Frontier!
Limited seats available. Enroll now and ship your first production LLM system.
- Live Online Classes
- Practitioner Instructors
- Production-Grade Projects
- 12-Week Multi-Agent Capstone