Specialist AI Engineering Tracks. Built For The AI Era.
Beyond the Bootcamp and the Advanced AI Engineering Program — four deep, specialist tracks for people who want to go all-in on one AI-era career path. Real tools, real projects, a real deployed outcome.
Become a 10X Developer
Ship production software at the speed of thought — using AI coding tools as your default workflow, not a party trick. Develop, debug and deploy real applications with GitHub Copilot, Cursor, Claude Code and more.
👤 Who it's for: Developers, students and builders who want to multiply their output with AI-native coding workflowsCourse Modules
Prompt-first development, thinking in specs and intents instead of syntax, and the generate → run → refine loop that makes AI coding tools reliable instead of chaotic.
- From spec to working code — writing prompts an AI can actually build from
- Knowing when to trust AI output vs. when to verify it line by line
- The iterative loop: generate, run, read the error, refine
Deep, hands-on setup and comparison of the tools actually used by AI-native developers today.
- GitHub Copilot — completions, chat, workspace-aware edits
- Cursor — codebase-aware chat, multi-file edits, custom rules
- Claude Code — agentic terminal-based development
- Windsurf & Cody — alternative workflows and when to reach for them
Generating full app skeletons, UI components and API endpoints from natural language — and making multi-file, codebase-aware edits safely.
- Scaffolding a frontend, backend and database layer from one prompt chain
- Generating and wiring up UI components from a design brief
- Codebase-aware edits that touch multiple files consistently
Feeding stack traces and logs to AI, root-causing bugs fast, and safely refactoring code you didn't write.
- Reading and explaining unfamiliar codebases with AI assistance
- Root-causing bugs from stack traces and log output
- Refactoring legacy code without breaking what already works
Generating meaningful tests, running AI-powered code review, and catching security issues before they're committed.
- Generating unit and integration tests from existing code
- AI-assisted code review — catching bugs and smells before a human does
- Basic security scanning as part of your everyday workflow
AI-written commit messages and PR summaries, automated review bots, and CI/CD pipelines scaffolded by AI.
- AI-generated commit messages and pull request summaries
- Automated code review bots in your PR workflow
- CI/CD pipelines and one-click deploys, scaffolded with AI assistance
Everything from the earlier modules, combined — idea to a live, deployed product, using the entire AI toolchain end to end.
AI DevOps Engineer
Master the infrastructure that runs modern software — containers, orchestration and cloud pipelines — with AI copilots accelerating every step, including deploying AI workloads themselves.
👤 Who it's for: Developers and IT professionals who want to own deployment, infrastructure and reliability for real applicationsCourse Modules
Linux and shell fundamentals, Git workflows for teams, and using AI copilots for scripting and infrastructure automation.
- Linux essentials and shell scripting for ops work
- Git branching workflows for infrastructure & application teams
- Using AI to write and review automation scripts
Images vs. containers, writing efficient Dockerfiles, multi-stage builds, and orchestrating multi-service apps with Docker Compose.
- Writing and optimizing Dockerfiles, including multi-stage builds
- Docker Compose for multi-service local development
- AI-assisted Dockerfile generation and image-size optimization
Pods, deployments, services and ingress, ConfigMaps & Secrets, Helm charts, and how Kubernetes self-heals and autoscales.
- Core objects: Pods, Deployments, Services, Ingress
- Configuration & secrets management
- Helm charts, autoscaling and self-healing in practice
Build and release pipelines, YAML pipelines, artifacts, environments and approval gates.
- Writing YAML pipelines for build, test and release
- Managing artifacts, environments and approval gates
- Integrating automated tests into every pipeline run
Terraform fundamentals, Azure Bicep/ARM basics, and using AI to generate and review infrastructure code.
- Core Terraform — providers, state, modules
- Azure Bicep/ARM templates for native deployments
- AI-assisted IaC generation and drift detection
Azure Monitor, Prometheus & Grafana, centralized logging, RBAC, secrets management, and keeping cloud spend under control.
- Monitoring & alerting with Azure Monitor and Prometheus/Grafana
- RBAC, secrets management and container security scanning
- Cost visibility, budgets and optimization strategies
Containerize an AI/LLM-powered application and deploy it to Kubernetes with autoscaling, monitoring and a CI/CD pipeline behind it.
AI Full Stack Engineer
Build complete, production-grade applications — frontend to database — with AI features built in from day one, not bolted on afterward.
👤 Who it's for: Developers who want to build real products end-to-end and ship AI-native features like chat, search and automationCourse Modules
React and Next.js fundamentals, TypeScript, component architecture and state management, with AI-assisted UI generation.
- React & Next.js core concepts, routing and data fetching
- TypeScript for safer, more maintainable frontend code
- Component architecture and state management patterns
Node.js and Python API development, REST and GraphQL design, and authentication & authorization done properly.
- Building REST and GraphQL APIs with Node.js/Python
- Authentication & authorization — JWT, OAuth, sessions
- API documentation and versioning basics
Relational design with PostgreSQL/MySQL, NoSQL with MongoDB, ORMs, and schema migrations that don't break production.
- Relational schema design and normalization
- NoSQL data modeling with MongoDB
- ORMs (Prisma/SQLAlchemy) and safe schema migrations
Adding chat and AI features to an application — streaming responses, function calling, and retrieval-augmented generation over your own data.
- Adding a streaming AI chat feature to a real app
- Function/tool calling from within your product
- In-app RAG — answering questions from your own data
WebSockets for live features, caching with Redis, and background jobs & queues for anything too slow to run inline.
- Real-time features with WebSockets
- Caching strategies with Redis
- Background jobs and queues for async work
API versioning & rate limiting, automated testing, Dockerizing the full stack, and deploying to production.
- API versioning and rate limiting for real users
- Automated testing across frontend and backend
- Dockerizing and deploying a full-stack app (Vercel/Render/VPS)
Everything combined — a real database schema, a secure API, a polished frontend, a working AI feature, and a live deployment.
AI Full Stack & Cloud Engineer
The complete builder's path — full-stack development and cloud architecture combined, so you can design, build and run AI-native systems end to end.
👤 Who it's for: Engineers who want both product-building and cloud/infrastructure skills in one track, not two separate coursesCourse Modules
Condensed frontend (React/Next.js) and backend (Node.js/Python) foundations, built for engineers heading straight into cloud architecture.
- React/Next.js essentials for building real UIs fast
- Node.js/Python API basics for a working backend
Core Azure/AWS services — compute, storage, networking and IAM — and when to reach for managed vs. self-managed services.
- Compute, storage and networking fundamentals
- Identity & access management (IAM) done right
Managed Postgres/MySQL, Cosmos DB/DynamoDB, managed caching, and vector databases for AI workloads.
- Managed relational and NoSQL databases in the cloud
- Managed caching layers
- Vector databases for AI-powered search and retrieval
Docker fundamentals, managed Kubernetes (AKS/EKS), and serverless functions — and how to choose between them.
- Docker fundamentals for cloud-native apps
- Managed Kubernetes on AKS/EKS
- Serverless functions (Azure Functions/AWS Lambda)
LLM APIs, RAG pipelines connected to your cloud database, and agent/tool-calling patterns in a cloud-native app.
- Calling LLM APIs from a cloud-hosted application
- RAG pipelines backed by your cloud vector store
- Agent and tool-calling patterns in production
GitHub Actions/Azure DevOps pipelines, Terraform for repeatable environments, and blue-green/canary deployments.
- CI/CD pipelines with GitHub Actions or Azure DevOps
- Terraform for repeatable, version-controlled infrastructure
- Blue-green and canary deployment strategies
A full application, containerized, deployed to a managed Kubernetes cluster, with CI/CD and an integrated AI feature — secured and monitored.