AI engineering has no government license — competence is shown through vendor and platform certifications. This is an overview of the certifications that matter for AI/LLM engineers and practitioners building with generative AI, what each covers, who runs it, and how to prepare.
There is no “PE for AI.” Instead, the market recognizes cloud-platform certifications (Microsoft Azure AI Engineer) for building generative-AI and LLM-backed applications, and specialist credentials (NVIDIA DLI) for deep-learning and accelerated-computing fundamentals. For data science and machine-learning modeling certifications (AWS ML Specialty, GCP ML Engineer, TensorFlow Developer, Databricks ML), see the AI and Data Science Studio.
Building AI solutions with Azure Cognitive Services, OpenAI and ML.
Specialized credentials in deep learning, accelerated computing and GenAI.
| Credential | Prerequisite | Typical experience | Administered by |
|---|---|---|---|
| Azure AI Engineer (AI-102) | Azure + coding | Varies* | Microsoft |
| NVIDIA DLI | Python + deep-learning basics | Varies by track* | NVIDIA |
* Experience hours and prerequisites vary significantly by state, jurisdiction and credential level. Figures shown are typical ranges, not legal requirements.
These certs reward hands-on skill. A portfolio of agents, RAG pipelines, and LLM-backed apps you actually built and deployed teaches the exam material faster than reading — and helps your career more.
Cloud AI exams test the vendor’s tooling (Azure Cognitive Services, Azure OpenAI). Know the managed services, not just generic AI theory.
Most of these are computer-based at proctored centers. Take full-length, timed practice exams on screen so pacing and exam-day logistics aren’t a surprise.
Vendors are rapidly adding generative-AI and LLM content (Azure OpenAI, Vertex, Bedrock). Study the current exam guide — blueprints change frequently.