Codenetix Applied AI Engineer Fellowship
A 12‑month, outcome‑driven pathway that blends AI fundamentals, Azure AI engineering, generative AI, cloud, and DevOps into a single AI Implementation Engineer track.
Who This Program Is For
Experienced professionals who want to move beyond theory and learn to build, ship, and operate real AI systems.
- Software developers and backend engineers
- Cloud / DevOps / platform engineers
- Data / analytics / BI professionals
- IT professionals with strong programming foundations
Program Snapshot
- 6 months intensive live training
- 6+ months internship / residency
- 10–15 real AI systems in your portfolio
- Curriculum aligned with Azure AI & cloud certifications
Curriculum Breakdown
Phase 1 — AI Foundations
Duration: 4 weeks
Solid foundations in Python, ML, and math for AI engineering.
- Python for AI, NumPy, Pandas
- Data preprocessing, feature engineering
- Supervised & unsupervised learning
- Evaluation metrics and model selection
- Git, GitHub, Docker basics
Phase 2 — Machine Learning Engineering
Duration: 4 weeks
Building robust ML pipelines that go beyond notebooks.
- Scikit‑learn pipelines & data flows
- Hyper‑parameter tuning, cross‑validation
- Deep learning introduction with PyTorch
- Churn, fraud, and recommender systems
- Basic ETL and data engineering concepts
Phase 3 — Generative AI Engineering
Duration: 6 weeks
Modern LLM‑driven systems with Azure OpenAI and open‑source tools.
- Transformers, embeddings, and tokenization
- Prompt engineering & evaluation
- RAG architectures & vector databases
- LangChain, LlamaIndex, agent frameworks
- Azure OpenAI integration & guardrails
Phase 4 — AI Application Development
Duration: 6 weeks
Turn models into full AI products used by end‑users.
- FastAPI for AI backends
- REST / GraphQL API patterns for AI
- React‑based conversational UIs
- Designing AI copilots & assistants
- Authentication, authorization, and security basics
Phase 5 — Production AI Systems
Duration: 4 weeks
Cloud, deployment, and DevOps for reliable AI systems.
- Azure compute, storage, and networking
- Containerization and orchestration fundamentals
- CI/CD pipelines for AI applications
- Monitoring, logging, and observability
- Responsible AI & governance practices
Phase 6 — Capstone & Residency
Duration: 4 weeks + 6+ months
Production‑grade capstone plus extended internship / residency.
- End‑to‑end AI product build
- Industry‑style code reviews
- Demo day with mentors / panel
- Placement into internship / residency
- Performance‑based full‑time conversion
Aligned with Key Azure Certifications
The curriculum is aligned with core Microsoft Azure tracks so that participants can optionally sit for certifications as a by‑product of the skills they build.
- AI‑900 — Azure AI Fundamentals
- AI‑102 — Azure AI Engineer Associate
- AZ‑104 — Azure Administrator (cloud foundations)
- AZ‑400 — DevOps Engineer (deployment pipelines)
Additional specialization paths such as DP‑100, AZ‑305, and Fabric Analytics can be layered for interested participants.
Outcomes Over Exams
The fellowship is positioned as a career accelerator, not a certification coaching center. The focus is on the ability to design, implement, and operate AI systems in production.
- Architect and implement RAG systems
- Deploy AI workloads on Azure securely
- Integrate LLMs into real products
- Collaborate with product and engineering teams