Builds applications on top of LLMs and diffusion models: RAG, agents and generation.
Entry
Intermediate
Coding
Very High
Key skills
Common progression
- NLP Engineer
- Generative AI Engineer
- AI Solutions Architect
- Solutions Architect
Career overview
The Generative AI Engineer builds applications on top of large language models and diffusion models: retrieval-augmented generation, agents, fine-tuning, evaluation and guardrails. They combine ML, software engineering and product thinking. The role leads to AI Solutions Architect, Senior ML Engineer or AI Product Manager positions.
Main responsibilities
- Build generative AI applications
- Serve model inference at scale
- Design and document REST APIs
- Write, test and review production code
- Build and maintain CI/CD pipelines
- Monitor platform health, alerts and dashboards
- Keep technical documentation up to date
- Prepare and execute change requests in production
- Analyse application logs to identify root causes
- Ensure responsible and safe AI use
Technical skills
Technologies
Education level
Education requirements vary depending on the company, country and experience. Professional experience, certifications and technical skills can sometimes compensate for formal education.
High School / Bac
Less common
Bac+2 (2-year degree)
Less common
Bac+3 (Bachelor)
Less common
Bac+5 (Master / Engineering)
Common entry point
Experience & coding level
Experience level
Coding level
Useful certifications
Certifications are never mandatory. They are one way among others to prove a skill.
- Microsoft AI Engineer Associate (AI-102)
- Google Generative AI Leader
- AWS Machine Learning Specialty
Where can this career take you?
Multiple paths are possible. Click a role to open its profile.
Direct progression
Possible branches
Where can you go next?
Realistic next career moves, with compatibility and a transition roadmap for each.
Overlap: 63% · Difficulty: Easy
Skills to learn: AI / LLM integration, Model serving, Cloud AI services
View roadmapOverlap: 11% · Difficulty: Hard
Skills to learn: AI solution architecture, ML & LLM landscape, Cloud AI platforms
View roadmapOverlap: 12% · Difficulty: Medium
Skills to learn: AI product strategy, Roadmapping, AI/ML literacy
View roadmapWhere can you go from here?
A typical progression built from real career relationships. Click any role to open its profile.
- NLP Engineer
- Generative AI Engineer
- AI Solutions Architect
- Solutions Architect
Soft skills
- Technical curiosity
- Creativity
- Problem solving
- Clear communication
- Sense of ownership
- Ethics and integrity
Common previous careers
Roles that frequently lead into this career.
Overlap: 61% · Difficulty: Easy
Skills to learn: LLM application design, RAG & agents, Responsible AI
View roadmapOverlap: 24% · Difficulty: Medium
Skills to learn: LLM application design, RAG & agents, Prompt engineering
View roadmapContinue your career exploration
Keep moving: compare roles, follow a roadmap, read a guide or prepare for an interview.
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A documented transition from this role to the next.
Generative AI EngineerAI Engineer
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