Career Roadmap
Generative AI Engineer: Zero to Hero
This path is about building applications powered by large language models. Focus on real systems, not just prompts. Learn step by step, then validate your understanding with certification-focused practice.
Embark on your career roadmap by setting a target and staying accountable
Set targetStep 0 - Basics
Build the foundation before working with AI systems.
2-4 weeks2-4 weeks
Step 0 - Basics
Build the foundation before working with AI systems.
- Python basics
- APIs and HTTP basics
- JSON and data handling
💡 You don’t need deep math, but you should be comfortable writing simple scripts.
Step 1 - AI and LLM fundamentals
Understand how modern AI systems work.
2-3 weeks2-3 weeks
Step 1 - AI and LLM fundamentals
Understand how modern AI systems work.
- What are LLMs
- Tokens and embeddings
- Prompting basics
- Limitations of LLMs
💡 Use ExamOS quizzes to check conceptual understanding.
Step 2 - Working with APIs (Azure OpenAI / Bedrock)
Start building real applications using managed AI services.
3-4 weeks3-4 weeks
Step 2 - Working with APIs (Azure OpenAI / Bedrock)
Start building real applications using managed AI services.
- Azure OpenAI basics
- AWS Bedrock basics
- Calling LLM APIs
- Handling responses
Certifications
💡 Focus on building small working apps, not just reading docs.
Step 3 - Prompt engineering and system design
Improve how you interact with models and structure outputs.
2-3 weeks2-3 weeks
Step 3 - Prompt engineering and system design
Improve how you interact with models and structure outputs.
- Prompt patterns
- Few-shot prompting
- Output structuring
- Guardrails
💡 This is where output quality improves significantly.
Step 4 - RAG (Retrieval-Augmented Generation)
Build systems that use external data with LLMs.
4-6 weeks4-6 weeks
Step 4 - RAG (Retrieval-Augmented Generation)
Build systems that use external data with LLMs.
- Embeddings and vector databases
- Document ingestion pipelines
- Search and retrieval
- RAG architecture
💡 This is one of the most important real-world skills.
Step 5 - Application development
Build full applications around LLMs.
3-4 weeks3-4 weeks
Step 5 - Application development
Build full applications around LLMs.
- Backend APIs
- Chat applications
- Integration with databases
- Error handling
💡 Focus on building something end to end.
Step 6 - Scaling and production systems
Make your applications reliable and scalable.
3-4 weeks3-4 weeks
Step 6 - Scaling and production systems
Make your applications reliable and scalable.
- Rate limiting and retries
- Caching responses
- Cost management
- Monitoring usage
💡 Production systems are about stability, not just features.
Step 7 - Security and responsible AI
Build safe and secure AI systems.
2-3 weeks2-3 weeks
Step 7 - Security and responsible AI
Build safe and secure AI systems.
- Prompt injection risks
- Data privacy
- Content filtering
- Responsible AI practices
💡 Security is becoming critical in GenAI systems.
Final step - Certification and validation
Before attempting any certification, build at least 2–3 real GenAI applications and run multiple ExamOS quizzes. Focus on architecture, not just prompts. If you can explain your system end to end, you're ready.
Final step - Certification and validation
Before attempting any certification, build at least 2–3 real GenAI applications and run multiple ExamOS quizzes. Focus on architecture, not just prompts. If you can explain your system end to end, you're ready.
Certifications
Realistic timeline
- 2 hours/day: around 4-6 months
- 3-4 hours/day: around 3-4 months
- Consistency matters more than intensity.
Embark on your career roadmap by setting a target and staying accountable
Set target