After the Roadmap

An honest assessment of where you'll stand after completing all 7 phases — what you'll know, what gaps remain, and what roles you'll be ready for.

This is a benchmark, not a score. Use it to set expectations and identify where to focus after the roadmap.

AI Engineer (Solid)

65%

Upper end of the 'Engineer' tier — approaching 'Specialist' in some areas

HobbyistBeginnerPractitionerEngineerSpecialistResearcher

How You Compare

AI Hobbyist10%

Uses ChatGPT, maybe tried some tools

Prompt Engineer30%

Good at prompting, no engineering depth

After this roadmap →65%

Builds apps, understands systems, reads papers

Senior ML Engineer80%

5+ yrs exp, production systems, ML depth

AI Researcher90%

PhD level, novel research, frontier models

Knowledge by Area

LLM Concepts & Internals80%

Strong. You can explain transformers, attention, training, RLHF — not just use them.

Prompt Engineering90%

Near expert. You'll have spent real hands-on time with multiple techniques.

RAG Systems80%

Strong. Can design, build, and evaluate a production RAG pipeline.

Agentic AI / Tool Use75%

Good practitioner level. Can build complex agents, understand design patterns.

Fine-Tuning / Training60%

Solid understanding, practical LoRA/QLoRA experience. Not a full ML researcher.

Multimodal AI40%

Awareness level. Know how diffusion works conceptually, limited hands-on.

ML Research / Math30%

Surface level. Can read papers but not write or advance them.

Production / MLOps50%

Moderate. Know the concepts, some hands-on but not deep deployment experience.

AI Safety / Ethics40%

Awareness. Know the issues, not a practitioner in alignment or safety research.

What you CAN do confidently

Build end-to-end AI-powered applications from scratch
Design and evaluate RAG pipelines for real use cases
Build multi-step agents with tool calling and memory
Choose the right model (API vs local vs fine-tuned) for a problem
Fine-tune open-source LLMs using LoRA/QLoRA on modest hardware
Read and roughly understand most AI research paper abstracts
Evaluate LLM outputs meaningfully (not just 'does it look good')
Discuss AI architecture decisions with engineers and researchers
Contribute meaningfully to AI discussions and design reviews

What you still can't do (yet)

Train a foundation model from scratch (needs massive compute + team)
Write or publish novel AI research
Deeply understand all the math (backprop derivations, probability theory)
Specialize in computer vision, audio, or robotics without extra study
Build highly optimized inference pipelines (vLLM, CUDA kernels, etc.)
Work as an ML researcher at a frontier lab (OpenAI, Anthropic, DeepMind)
Architect enterprise-scale AI systems without production ML experience

What's Next

Deepen by Building Publicly

Immediately & ongoing

Fill the Math Gap (Selectively)

After Phase 4–5, if curious

Start Reading Papers

After Phase 5

Pick a Specialization

After completing the roadmap

Engage with the AI Community

Throughout the journey

Keep a 'Learning Radar'

Ongoing forever

The Honest Reality

After this roadmap, you'll have hands-on experience with every major layer of the AI stack — from prompting to production.

But AI is a field, not a course. The best practitioners treat it as a continuous practice — building things, reading papers, and re-learning as the landscape shifts every 6 months.

The gap between a 65% AI Engineer and a 90% Researcher isn't really about courses. It's about years of building real systems and going deep on one specific problem that matters to you.

Your developer instincts are your biggest asset. Most AI courses are taught to people who can't code. You can. That alone puts you 2 years ahead.