AI Engineer → Generative AI Engineer
A realistic transition from AI Engineer to Generative AI Engineer: transferable skills, the gaps to close, a step-by-step learning plan and alternative routes. Estimated skill overlap 50%. Difficulty: Easy.
Difficulty: Easy6 stepsDuration: 4 to 8 monthsOverlap: 50%
Transition from AI Engineer to Generative AI Engineer.
Transferable skills
- LLM application design
- Python
- Prompt engineering
- Evaluation & guardrails
Skills to learn
- RAG & agents
- Fine-tuning / LoRA
- Vector databases
- Responsible AI
Step-by-step learning plan
1. Build on your AI Engineer experience
2–4 weeks
Leverage
- LLM application design
- Python
- Prompt engineering
- Evaluation & guardrails
Map transferable skills — LLM application design, Python, Prompt engineering — against what a Generative AI Engineer needs.
2. Learn the core of the Generative AI Engineer role
6–10 weeks
Learn
- RAG & agents
- Vector databases
Technologies
Anthropic APIMilvusAzure OpenAI
Cover the biggest gaps first: RAG & agents, Vector databases.
3. Master advanced Generative AI Engineer skills
6–10 weeks
Learn
- Fine-tuning / LoRA
- Responsible AI
Technologies
Guardrails AI
Handle the advanced topics: Fine-tuning / LoRA, Responsible AI.
4. Get productive with the Generative AI Engineer toolchain
4–8 weeks
Technologies
Anthropic APIMilvusAzure OpenAIGuardrails AI
Be hands-on with Anthropic API, Milvus, Azure OpenAI and the rest of the target stack.
5. Practise Generative AI Engineer work in real conditions
4–8 weeks
Ship a portfolio project or take on Generative AI Engineer tasks at work.
6. Get certified and position yourself as a Generative AI Engineer
4–8 weeks
Technologies
Microsoft AI Engineer Associate (AI-102)Google Generative AI LeaderAWS Machine Learning Specialty
Update your CV and LinkedIn around Generative AI Engineer, then apply to target roles.