Prebuilt Projects2Jobs roadmap
AI Engineer Roadmap
Build hiring proof for AI Engineer roles: building, evaluating, and shipping machine learning systems.
AI Engineer candidates and career switchers targeting AI / Machine Learning roles.
Timeline
8 weeks
Level
Intermediate
Final outcome
A AI Engineer portfolio with shipped projects, public GitHub proof, resume bullets, and interview talking points.
Skills to prove
Python
PyTorch or TensorFlow
Data preparation
Model evaluation
LLM APIs
Experiment tracking
Portfolio projects
- End-to-end ML project from dataset to deployed model
- LLM-powered application with evaluation and guardrails
- Reproduction of a paper result with an ablation writeup
Prebuilt build path
Follow these phases in order. Each one ends with a portfolio artifact you can show in GitHub, on your resume, or in interviews.
Step 1
Weeks 1-2
Build a rigorous baseline
Show ML discipline, not just model calls.
- Pick a problem, establish a simple baseline, and set up experiment tracking.
- Document the dataset, splits, leakage risks, and evaluation metrics.
Deliverable: A tracked baseline with honest evaluation.
Step 2
Weeks 3-6
Improve it and ship it
Prove you can iterate and productionize.
- Beat the baseline with documented experiments and error analysis.
- Deploy the model or LLM app behind an API with monitoring and a demo UI.
Deliverable: A deployed ML system with experiment history and an evaluation report.
Step 3
Weeks 7-8
Package the proof for hiring
Turn the work into evidence recruiters and interviewers can verify quickly.
- Polish each repo README with screenshots, setup steps, architecture notes, and tradeoffs.
- Write resume bullets and interview talking points that map each project to AI Engineer job requirements.
Deliverable: Public GitHub proof, an updated resume, and interview-ready project stories.
Make it personal
Projects2Jobs compares this roadmap to your resume, current skills, and existing projects, then generates a role-specific build plan.
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