Career Roadmap
AWS AI Engineer: Zero to Hero
This is not just about models. It is about getting models into production, making them useful, and keeping them running. Build real skills step by step. Use certifications to confirm you are actually ready.
Embark on your career roadmap by setting a target and staying accountable
Set targetStep 0 - Basics
Build a foundation before touching AWS AI services.
2-4 weeks2-4 weeks
Step 0 - Basics
Build a foundation before touching AWS AI services.
- Python (basic scripting, functions)
- Data basics (CSV, JSON, pandas basics)
- Basic cloud concepts
💡 Skipping this makes everything later harder.
Step 1 - AWS fundamentals
Get comfortable with core AWS services before moving into AI.
2-3 weeks2-3 weeks
Step 1 - AWS fundamentals
Get comfortable with core AWS services before moving into AI.
- S3
- IAM
- EC2
- Basic networking
Certifications
💡 Use ExamOS quizzes to make sure you understand IAM and cost basics before moving on.
Step 2 - AI foundations
Understand what AI and ML actually mean before building with them.
2-3 weeks2-3 weeks
Step 2 - AI foundations
Understand what AI and ML actually mean before building with them.
- Supervised vs unsupervised learning
- Model training basics
- Generative AI basics
- Responsible AI
Certifications
💡 Use ExamOS quizzes here to check if your concepts are clear.
Step 3 - Machine learning on AWS
Start building using the full ML lifecycle on AWS.
4-6 weeks4-6 weeks
Step 3 - Machine learning on AWS
Start building using the full ML lifecycle on AWS.
- Amazon SageMaker
- Data preparation
- Model training and tuning
- Deployment (real-time and batch)
💡 This is the core skill. Use ExamOS quizzes to test real-world scenarios.
Step 4 - MLOps and production systems
Learn how to run and maintain models in real environments.
3-4 weeks3-4 weeks
Step 4 - MLOps and production systems
Learn how to run and maintain models in real environments.
- Pipelines (training and deployment)
- Model versioning
- Monitoring and drift detection
- Security and IAM for ML
💡 Building a model is one part. Keeping it running is the real challenge. Use ExamOS quizzes to validate your understanding.
Step 5 - Generative AI
Move into modern AI systems used in real applications today.
4-6 weeks4-6 weeks
Step 5 - Generative AI
Move into modern AI systems used in real applications today.
- Amazon Bedrock
- Foundation models
- RAG (retrieval-augmented generation)
- Vector databases
💡 Use ExamOS quizzes to test architecture and real-world GenAI scenarios.
Step 6 - Data engineering
OptionalStrong data skills make your AI systems more reliable and scalable.
3-4 weeks3-4 weeks
Step 6 - Data engineering
OptionalStrong data skills make your AI systems more reliable and scalable.
- Data pipelines
- ETL processes
- Data storage and governance
Certifications
💡 Not required, but this is where many engineers start to stand out.
Final step - Certification and practice
Before booking, run multiple timed ExamOS quizzes, focus on weak areas, and repeat until your scores stay consistent. If you can explain what you built and pass practice tests, you are ready.
Final step - Certification and practice
Before booking, run multiple timed ExamOS quizzes, focus on weak areas, and repeat until your scores stay consistent. If you can explain what you built and pass practice tests, you are ready.
Certifications
Realistic timeline
- 2 hours/day: around 5-6 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