What skills do I need to work on generative AI projects?

Summary

• Core generative AI engineering skills: building LLM-powered applications, developing retrieval augmented generation (RAG) applications, creating multi-stage reasoning pipelines, fine-tuning models for specific tasks, and applying responsible AI practices.
• Deployment and operations (LLMOps): deploying models at scale and monitoring generative AI solutions in production.
• Data foundations: comfort working with both structured and unstructured data, which generative AI applications are grounded in.
• The Generative AI Engineer role brings these skills together across solution design, development, deployment, and monitoring.
• Databricks provides the platform and the training: Mosaic AI (fine-tuning, vector search, the Agent Framework, and model serving) plus Databricks Academy courses and a Generative AI Engineer Associate certification to build and validate these skills.

What skills do I need to work on generative AI projects?

Working on generative AI projects combines applied AI-engineering skills with solid data foundations. Databricks organizes these into the Generative AI Engineer role, which spans designing, developing, deploying, and monitoring generative AI solutions. The good news is you can learn every skill below through free, structured training and practice them hands-on on the Databricks Data Intelligence Platform.

Core skills for generative AI projects

• Building LLM applications. Assemble applications on top of large language models, including using open-source model libraries such as Hugging Face.
• Retrieval augmented generation (RAG). Ground model responses in your own data by retrieving relevant context at query time — a foundational pattern for enterprise generative AI.
• Multi-stage reasoning pipelines. Chain models, tools, and retrieval steps together (for example, with LangChain) to build compound AI systems that handle complex tasks.
• Fine-tuning models. Adapt foundation models to specific tasks and domains using your proprietary data.
• Responsible AI. Apply practices for safety, quality, and governance when building with LLMs.
• Deployment and LLMOps. Deploy models at scale and apply LLMOps best practices to monitor and manage generative AI solutions in production.
• Data foundations. Because generative AI is grounded in your organization's data, comfort working with structured and unstructured data is essential.

How Databricks supports generative AI teams

Databricks lets you apply these skills end to end on one governed platform:

• Mosaic AI provides pre-training, fine-tuning, and deployment of generative AI models on scalable, secure infrastructure, plus highly scalable vector search and an AI Agent Framework for building compound AI systems.
• The platform supports structured and unstructured data, pre-integrates popular generative AI models, includes a built-in vector store for semantic search, and supports both batch and real-time workflows.
• Model serving and built-in LLMOps/MLOps capabilities cover the full lifecycle of your models.

Getting started

• Build foundational knowledge with the Generative AI Fundamentals course, covering practical AI applications, implementation strategies, and legal and ethical considerations.
• Go deeper with Generative AI Engineering with Databricks, which includes hands-on labs.
• Validate your skills with the Databricks Generative AI Engineer Associate certification, which is continuously updated as tools and approaches change.
• Explore all role-aligned learning paths in Databricks Academy.

FAQs

Do I need to be a data scientist to work on generative AI projects?

No. The Generative AI Engineer role emphasizes practical skills such as building LLM applications, RAG, fine-tuning, and deployment. Databricks Academy offers courses and a certification designed to build these skills from the fundamentals up.

What technical skills are most important?

Building LLM applications, retrieval augmented generation (RAG), multi-stage reasoning pipelines, fine-tuning models, applying responsible AI, and deploying at scale with LLMOps.

How can I prove my generative AI skills?

Databricks offers the Generative AI Engineer Associate certification, which validates competencies across generative AI solution design, development, deployment, and monitoring.