The 10x Engineer: Professional AI Certification

Master AI Agents and Automation through deep codebase understanding, custom workflows, and spec-driven development.

What you’ll learn
Master the use of AI coding agents to manage the full software development lifecycle, including robust building, testing, and deployment
Scale AI development by implementing custom agents, specialized tools (MCPs), and project-specific rules across large codebases
Configure autonomous workflows and webhooks to automate enterprise tasks like ticket resolution and pull request review
Apply Spec-Driven Development (SDD) alongside optimal LLM selection strategies to maximize capability and cost efficiency

Requirements
This is a hands-on series that assumes you are familiar with basic coding and development workflows. Learners are encouraged to install the Zencoder plugin for their preferred Integrated Development Environment (IDE), such as VS Code or JetBrains

Description
This course contains the use of artificial intelligence.This professional AI certification course, “The 10x Engineer: Professional AI Certification,” is designed to transform experienced engineers into masters of advanced software development through deep AI utilization and strategic adoption. Moving beyond basic tool usage, the curriculum focuses on customizing AI capabilities, maximizing efficiency, and implementing automated, end-to-end engineering workflows.Mastering Context and CustomizationLearners are equipped to achieve a deep codebase understanding across projects of any size. This involves leveraging tools like the Repo Info Agent to document the repository structure and the Multi-Repo Tool to enable agents to consult related codebases spread across multiple repositories.A core focus is on personalizing AI tools to fit specific project needs. You will learn to control agent behavior by establishing custom Instructions and Rules and by creating specialized Custom Agents tailored with specific toolsets, which can then be shared across your organization. Furthermore, the course teaches you to integrate external applications using Custom Tools (MCPs), enabling complex use cases such as converting detailed Figma designs directly into production-ready code. Crucially, you will gain expertise in optimizing agent performance by strategically selecting the best LLM models (e.g., Mini 2.5 Pro, Sonnet 4.5, GPT-5 Codeex) based on the task, required speed, quality, and cost efficiency.Implementing Strategic Development and AutomationYou will master Spec-Driven Development (SDD), a technique that significantly improves agent outcomes by structuring the development around a detailed specifications document (including Requirements, Design, and Plan). The course demonstrates how to integrate SDD with established methodologies like Test-Driven Development (TDD) and Behavior-Driven Development (BDD), and how to use agents to assist in drafting and researching these specs.The course culminates in implementing Autonomous Agents to automate entire end-to-end development cycles. This includes configuring complex flows using webhooks, Jira, and GitHub (or GitLab/Big Bucket) to automatically resolve assigned tasks, generate Pull Requests, and even configure secondary agents to autonomously review those generated PRs. This high level of automation is demonstrated through challenging use cases, such as directing a specialized agent to perform a complex code migration (e.g., Java 1.6 to Java 17).Driving Organizational AI AdoptionFinally, the certification addresses the strategic challenge of driving AI adoption within an engineering organization, recognizing it as fundamentally a “people challenge,” not a technology problem. Key strategies covered include:1. Champion Programs: Identifying and investing heavily in a small group of peer experts who spread knowledge organically.2. Communicating Successes: Consistently capturing and sharing specific, concrete wins (e.g., “Shara used AI to write 300 tests in two minutes”) to build social proof and momentum.3. Measurement: Utilizing usage dashboards to identify patterns, spot power users, and guide adoption efforts through informed conversations.By the end of the course, you will have both the advanced technical expertise to build software at 10x speed and the strategic knowledge required to implement and scale AI across any engineering team.

Who this course is for
This course is intended for a diverse audience within engineering organizations, ranging from hands-on Developers and Engineers to strategic Engineering Managers and Organizational Leaders focused on maximizing return on investment (ROI) from AI tools. Developers will find value in learning practical, hands-on techniques such as building and scaling applications using various AI agents, utilizing techniques like Spec-Driven Development (SDD), Test-Driven Development (TDD), and Behavior-Driven Development (BDD) to achieve better results. The course content is relevant for those tackling complex use cases, including code migration (e.g., Java 1.6 to Java 17), front-end development using tools like the Figma MCP, achieving deep codebase understanding for single or multi-repository projects, customizing agent behavior using instructions and rules, and creating custom tools or Multi-Context Providers (MCPs). Furthermore, learners involved in infrastructure, tooling, or DevOps will benefit from understanding how to configure autonomous agents to handle tasks like resolving Jira issues and generating pull requests autonomously. Leaders and strategists will benefit from the modules covering how to successfully drive AI adoption across an organization using change management strategies, implementing champion programs, communicating successes, and using measurement dashboards to unlock the full value of the investment.