AI Engineering Foundation:
- Python and data structures
- Object-oriented programming
- SQL and database fundamentals
- Probability, linear algebra and calculus
- Git, Linux and debugging
- Clear technical communication

Programming Start:
- Python variables, loops and functions
- Lists, dictionaries and classes
- APIs, JSON and HTTP basics
- Read a CSV and build a simple service
- Version control with Git and GitHub

AI Engineering Skills:
- Python, APIs and backend services
- SQL, data pipelines and validation
- Machine learning and evaluation
- LLMs, RAG, agents and tool use
- Docker, cloud and monitoring
- Security, privacy and responsible AI

AI Engineer Learning Order:
1. Python, Git and computer science basics
2. SQL, APIs and data handling
3. Machine learning fundamentals
4. LLM apps, RAG and evaluations
5. Deploy with Docker and cloud
6. Build production-style projects

Every AI Engineering Project Should Include:
1. Business question and dataset source
2. Data cleaning steps
3. Architecture and data flow
4. Metrics, tests and error analysis
5. README, setup steps and limitations
6. Deployment, monitoring and next steps

AI Engineering Portfolio Checklist:
- One ML model served through an API
- One RAG or LLM application
- One agent or automation workflow
- One containerized or cloud deployment
- Tests, logs and evaluation examples
- One-page resume with technical impact

AI Engineering Learning Timeline:

Months 1-4: Programming Foundation
- Python, Git, Linux and DSA basics
- OOP, testing and clean code
- Build small command-line projects

Months 5-8: ML Engineering
- SQL, Pandas, statistics and ML
- Build training and evaluation pipelines
- Track experiments and errors

Months 9-12: AI Applications
- LLM APIs, RAG, agents and tool calling
- Build one evaluated AI application
- Add authentication and basic security

Months 13-18: Production and Jobs
- Docker, cloud, monitoring and MLOps
- Deploy two production-style projects
- Prepare resume, interviews and applications

Job Preparation Guide:

Target Roles:
ML Engineer | AI Engineer | LLM Engineer | MLOps Associate

Resume Keywords:
Python | DSA | SQL | ML | LLMs | APIs | Docker | Cloud | MLOps

Interview Topics:
Python | DSA | ML metrics | RAG | APIs | System design | Security | Projects

Portfolio Proof:
ML API | RAG app | Agent workflow | Deployment | Tests | Monitoring

Helpful Certifications:
- Cloud, ML or AI engineering certification after project practice
- Open-source contribution or technical capstone
- Practical Python, APIs and deployment projects

AI Engineer Job Search Plan:

Months 1-8: Engineering Foundation
- Python, DSA, SQL and ML
- Build two reproducible projects
- Document tests, metrics and architecture

Months 9-12: AI Applications
- LLM APIs, RAG and agent workflows
- Build and evaluate one AI application
- Publish code and technical explanation

Months 13-18: Applications
- Resume, GitHub and mock interviews
- Apply to internships and junior engineering roles
- Network with AI practitioners

Target outcomes:
- Three documented AI engineering projects
- One deployed API or AI application
- Clear explanation of tests, limitations and impact

Recommended Resources:

Free Resources:
- Python documentation and practice notebooks
- Kaggle – Datasets and notebooks
- Scikit-learn user guide
- SQL practice datasets
- YouTube – Statistics and ML channels

Paid Resources:
- Uncodemy – AI and Python Engineering Training
- DataCamp – Analytics tracks
- Coursera – Specializations
- LinkedIn Learning – BI courses

Certifications (Recommended):
- Cloud, Python and AI engineering certification
- AI tools and no-code workflow practice