AI Pro Software Developer
Upskilling Course, 40-60 Academic Hours
Code Smarter. Lead Faster.
Warum sollten Sie sich für diesen Kurs anmelden?
AI is reshaping how software is built. Software Developers need to know what to build, how to guide AI systems, and how to ensure quality, correctness, and outcomes. Developers who can define intent, design architecture, and control AI implementation are becoming the ones who deliver faster, scale better, and lead engineering teams into the next era.
Wawiwa’s AI Pro Software Developer course is a practical, instructor-led upskilling course designed for experienced Software Developers. It builds a new way of working: managing AI to produce high-quality code under clear guardrails. You’ll move toward higher-leverage ownership-architecture, decision-making, and outcomes – so you can deliver more, faster, and without writing every line of code yourself.
This course may also be suitable for juniors; however, additional time may be required to complete exercises and tasks, which may extend the overall course duration.
By enrolling in this course, you'll gain practical skills to design systems, guide AI tools, and deliver reliable software faster.
Wer sollte teilnehmen?
- Backend, Frontend, or Full-Stack Developers
- Mid-level and senior Engineers responsible for delivery, system design, or architecture
- Technical leads and architects seeking repeatable AI-first development practices
- Engineers looking to shift from implementation-heavy work to higher-leverage ownership
Lernziele und Lernergebnisse
Am Ende des Kurses werden die Teilnehmer:
- Architect and plan systems using AI before writing code
- Delegate implementation to AI while maintaining full technical ownership
- Design, review, and validate AI-generated backend and frontend code
- Use advanced prompting, thread management, and model selection effectively
- Build production-grade systems with AI-assisted backend, API, and UI development
- Integrate AI-callable services and workflows into real-world applications
- Control quality, cost, risk, and observability in AI systems
- Apply a repeatable AI-first methodology that goes beyond specific tools and trends
Was Sie bekommen...
- 40-60 academic hours of live classes with a professional instructor, teaching in your local language.
- Zugang zu den Aufzeichnungen der Sitzungen für den Fall, dass Sie eine Sitzung verpasst haben oder zur Vertiefung, während der gesamten Kursdauer.
- Abschlusszertifikat, auch ein digitales für LinkedIn. Die Graduierung erfordert die Teilnahme an mindestens 80% der Kurse.
- Lebenslange Mitgliedschaft in der globalen Alumni-Gemeinschaft von Wawiwa, die sich aus Tech-Profis zusammensetzt, die Wawiwa's Umschulungs- und Weiterbildungsprogramme auf der ganzen Welt absolviert haben.
Professioneller Betreuer
Dieser einzigartige Kurs wurde von einem professionellen Team entwickelt, das sich aus führenden Experten in den Bereichen Softwareentwicklung und KI zusammensetzt und über umfangreiche Kenntnisse und Erfahrungen in der Ausbildung verfügt.
Rony Keren
Head of AI Pro Software Developer Course
Rony is a technology executive with over 20 years of experience driving innovation and scaling organizations in the global tech industry. He has a proven track record of leading engineering teams across multiple locations, shaping technology strategy, and enabling business growth through modernization, cloud adoption, and secure, scalable platforms.
Rony maintained and expanded the professional and educational offerings of John Bryce Training Center, a college in Israel, with a focus on the needs of the hi-tech industry.
He is passionate about aligning technology with business goals, delivering impact at scale, and mentoring the next generation of leaders.
Rony holds a Bachelor’s degree in Economics from Bar-Ilan University.
Kurs-Lehrplan
Introduction - The 10X AI Developer Mindset (4 Hours)
- From Software Developer to AI-first engineering leader
- AI as your engineering team, not just a tool
- Delivery ownership and quality responsibility
- Outcome-driven development
- Common AI misuse and anti-patterns
Software Architecture Musts - AI-First Engineering (6 Hours)
- Clean architecture and modular design
- Domain-Driven Design (DDD) foundations
- Core design patterns (repository, service, strategy, factory, adapter)
- API-first and contract-first design
- Event-driven architecture basics
- Designing testable and extensible systems
- Planning architecture with AI before coding
- AI-assisted architecture review and validation
How AI and Generative AI Work (4 Hours)
- LLM fundamentals (tokens, context, limitations, hallucinations)
- Model configuration (temperature, max tokens, top-p, system prompts)
- Working with models via APIs and consoles
- Retrieval-Augmented Generation (RAG), embeddings, and vector databases – when and why to use them
- Fine-tuning vs prompt engineering
- Model selection by use case
- Cost, latency, privacy, and governance trade-offs
- Provider landscape (AWS Bedrock, Azure OpenAI, Google Vertex AI / Gemini)
The Art of Prompting and Human-AI Collaboration (4 Hours)
- Prompting as specification design
- Role setting, constraints, and structured outputs
- Multi-step reasoning prompts
- Thread and context management
- When to continue vs restart interactions
- Model and workflow selection strategies
- Coding vs discussion workflows
- Quality standards (“god modes”)
- Failure modes and recovery techniques
Ongoing Project - AI-Assisted System Design (5 Hours)
- Project definition and scope
- Language-agnostic system specification
- Functional and non-functional requirements
- Architecture and data modeling
- API contracts
- Testing and validation strategy
- AI-assisted design review
Ongoing Project - Backend Core Implementation (6 Hours)
- Environment and infrastructure setup
- Database schema and migrations
- Repositories and services
- Core business logic
- AI-generated code review and refactoring
- Test generation and validation
MCP Servers - AI-Callable Systems (Optional, 4 Hours)
- Model Context Protocol (MCP) server concepts and use cases
- Designing AI-callable service layers
- Tool exposure and security considerations
- Language-agnostic implementation
- Connecting LLMs and vector databases
- Integrating MCP with AI development tools
Ongoing Project - MCP Integration (Optional, 5 Hours)
- MCP server implementation
- Backend integration
- AI-driven execution workflows
- Debugging and observability
Ongoing Project - API Layer (6 Hours)
- REST or GraphQL API design
- Contracts and schemas
- Authentication and authorization
- Error handling and versioning
- AI-assisted API testing
Full-Stack AI and Vibe Coding (4 Hours)
- Vibe coding principles and limitations
- Prompt-driven UI development
- Working with design systems
- Enforcing design tokens and component libraries
- Managing complex user flows
- Frontend quality standards
Ongoing Project - Full Working UI (4 Hours)
- Full frontend implementation
- Backend integration
- End-to-end user flows
- Error and edge-case handling
Ongoing Project - Rapid Feature Expansion (4 Hours)
- Adding backend features
- Extending frontend functionality
- Multi-feature delivery using AI
- Comparing speed and quality vs traditional development
Cross-Cutting 10X Topics (Applied Throughout) (6 Hours)
- AI code review
- Testing strategies in the AI era
- Cost and token optimization
- Prompt, model, and specification versioning
- Observability for AI systems
- Human-in-the-loop workflows
- Risk, safety, and compliance basics
- Tooling landscape (IDEs, agents, assistants)
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