Career Roadmaps
Structured, step-by-step learning paths. Every stage links to a free SuperML course or tutorial.
AI Engineer
From Python and ML fundamentals to LLMs, RAG, agentic AI, and MLOps in production.
8 stages · ~1 year full-time pace
🎓 Certification available View roadmap →Machine Learning Engineer
Python and math foundations through classical ML, deep learning, and production MLOps systems.
6 stages · 9–12 months part-time pace
🎓 Certification available View roadmap →Data Scientist
Python, SQL, and statistics through A/B testing, applied ML, and business communication.
8 stages · 7–10 months part-time pace
🎓 Certification available View roadmap →AI Research Scientist
Math and deep learning theory through paper reproduction, experimentation rigor, and novel model development.
8 stages · ~1–1.5 years full-time pace
View roadmap →MLOps Engineer
Experiment tracking and CI/CD through containerization, serving, registries, monitoring, and Kubernetes.
8 stages · 6–9 months part-time pace
View roadmap →AI Product Manager
AI/ML fundamentals and prompt fluency through evaluation, RAG/agent product patterns, and responsible AI.
8 stages · 6–9 months part-time pace
🎓 Certification available View roadmap →Forward Deploy Engineer
Separate paths for Software Engineers and Architects moving into customer-embedded delivery roles.
7 stages · 4–6 months part-time pace · 2 starting-role paths
🎓 Certification available View roadmap →Ready for the interview?
Every role above has a complete interview question guide — sample answers, frameworks, and a 2-week prep plan. Several also have a free, adaptive certification exam.