Become a Production-Ready
Agentic AI Engineer

A live, hands-on cohort that takes you from Python foundations to shipping autonomous AI agents with RAG, tools, memory, Docker, and cloud deployment — built the way real teams build.

Live Cohort (USA-friendly ET/PT)

Capstone + Code Reviews

Deploy to Cloud (Real Production Workflow)

Not bookish. Not demos-only. You'll build end-to-end systems with production patterns: observability, cost control, safety, and deployment.

What you'll be able to build (for real)

LLM Apps that Ship

Build reliable LLM features with guardrails, structured outputs, and evaluation.

RAG Systems with Accuracy

Vector search + retrieval design, chunking strategies, and response grounding.

Agentic AI Workflows

Tool calling, planning, memory, and multi-step autonomous execution.

Production APIs

Build FastAPI services with clean architecture, config, and error handling.

Dockerized Deployment

Containerize apps, run with Compose, deploy to a cloud VM with SSL.

Cost + Observability

Token cost control, logging/tracing, rate limits, and reliability patterns.

Why this isn't another "GenAI course"

Foundation-first

Python + APIs + deployment skills before GenAI/agents

Real architecture

Designed like product teams build systems

Hands-on labs

Every week: build, break, fix, deploy

Capstone portfolio

You can confidently show employers

USA-friendly schedule

With recordings for flexibility

Curriculum (Foundation → GenAI → Agentic AI)

Python fundamentals for engineers (clean code, OOP, exceptions, logging)
Working with APIs, JSON, files, environment variables
Writing maintainable modules and reusable utilities

Labs: CLI tools + API mini-projects

FastAPI fundamentals (routes, validation, error handling)
Async basics, background tasks (practical use)
Auth concepts (tokens/sessions overview)
Testing mindset + debugging workflows

Labs: Build a production-style API service

Docker fundamentals, Dockerfiles, multi-stage builds
Docker Compose: app + database + networking + volumes
Cloud basics: VM vs containers vs serverless (what teams use and why)
Deployment patterns: domains, SSL, environment configs, reverse proxy basics

Labs: Deploy your containerized app to a cloud VM

Tokens, embeddings, context windows, structured outputs
Prompting that works in production (constraints + reliability)
Choosing models: hosted vs open-source (cost/latency tradeoffs)

Labs: Build AI endpoints and production prompt patterns

Chunking strategies (what works and why)
Vector DB: pgvector + retrieval patterns
Hybrid search concepts, reranking overview
Evaluation basics: precision/recall mindset for retrieval quality

Labs: Build a RAG app on your own documents

What agentic AI really means (tools + planning + memory)
Tool calling architecture (APIs, browsers, internal tools)
Memory: short-term vs long-term, summarization strategies
Multi-agent collaboration (roles, routing, delegation)
Safety patterns: prompt injection defense, data leakage prevention

Labs: Build an autonomous research/report agent

Choose one capstone track:

Enterprise Knowledge Assistant (RAG + eval + guardrails)
Autonomous Research & Reporting Agent
Agentic Workflow Automation (email/docs/CRM simulation)

Deliverables:

GitHub repo
Deployable app
Demo script + architecture diagram
Resume/portfolio bullet guidance

Projects you'll ship

Enterprise RAG Knowledge Assistant

Upload docs → index → retrieve → answer with citations
Evaluation checks + safety guardrails

RAG pgvector FastAPI Docker

Autonomous Research + Report Agent

Multi-step planning, tool usage, report generation
Memory + structured outputs

Agents Tools Memory Reliability

Agentic Workflow Automation

Task routing + tool calling (simulated business workflow)
Logs + retry policies + cost tracking

Orchestration Observability Production Patterns

Meet your Instructor

Mohammed Sayeeduddin

AI Systems Architect | Enterprise Trainer | 16+ Years in IT

12+ years delivering corporate training across modern engineering stacks
Trained professionals from global enterprises including Capgemini, CGI, LTI Mindtree, ITC Infotech and others
Builds real-world systems: Dockerized apps, cloud deployments, AI workflows, and agentic architectures
Teaching style: hands-on, engineering-first, production-ready

No buzzwords. We build systems you can deploy and explain confidently.

Cohort format (USA-friendly)

Duration

12–14 weeks

Live sessions

3×/week

Session length

~2.5 hours

Timezone

ET/PT options
(details shared after signup)

Recordings

Included

Seat limit

30
(to keep reviews meaningful)

Launch Offer (USA Cohort)

$999 $1499

Included:

Live cohort sessions (ET/PT friendly)
Full recordings library
Hands-on labs + assignments
Capstone project + review
Private cohort community
Certificate of completion

Refund assurance: Attend the first 2 sessions. If it's not the right fit, you can request a refund.

Limited seats to maintain quality feedback and project reviews.

Frequently Asked Questions

No. We start with the engineering foundations and build up to GenAI and agents.

Basic programming helps, but we cover Python foundations early and build fast with labs.

Yes — but it's serious. You'll write code, ship projects, and deploy.

Yes. You'll Dockerize and deploy a working app with production-style configuration.

Recordings are included, and you can keep pace with weekly checkpoints.

Live sessions + 4–6 hours/week for labs (depends on your background).

Yes — certificate of completion after you finish required labs and capstone.

You'll leave with a deployable portfolio project, architecture understanding, and practical patterns employers expect.

Become a Production-Ready Agentic AI Engineer