Large language model foundations
Importance: Essential (5 of 5)
Understands transformer behavior, context, limitations, and model selection.
← All career guides9 of 9 skills31 of 32 courses
Data and AI
AI Engineer
Builds and operates AI-enabled applications using foundation models, retrieval, agents, evaluation, and safety controls.
Skill coverage
100%
Course coverage
97%
Skill coverage shows how much of the role's capability map already has a published course. Course coverage shows how much of the mapped course pipeline for this role has shipped.
- Role skills
- 9
- Published coverage
- 9
- Relevant courses
- 31
Capability map
Skills
Importance describes how central each capability is to the role. Course coverage shows where you can build the skill in the current OpenSkills catalog.
01
Foundation-model applications
4 capabilities
Prompt engineering
Importance: Very important (4 of 5)
Designs structured model instructions and interaction patterns.
Course coverage
Retrieval-augmented generation
Importance: Essential (5 of 5)
Grounds model responses in retrieved authoritative information.
Course coverage
AI agents
Importance: Very important (4 of 5)
Builds controlled tool-using and multi-step AI systems.
02
AI production
5 capabilities
AI evaluation
Importance: Essential (5 of 5)
Evaluates task quality, safety, regressions, and model trade-offs.
Course coverage
Model adaptation
Importance: Important (3 of 5)
Selects prompting, retrieval, and fine-tuning appropriately.
Course coverage
AI security
Importance: Essential (5 of 5)
Mitigates prompt injection, data leakage, unsafe actions, and supply risks.
Course coverage
Generative AI operations
Importance: Essential (5 of 5)
Operates AI applications with observability, cost, release, and incident controls.
Course coverage
- Generative AI Operations
- Machine Learning Model Monitoring
- Machine Learning Model Serving
- MLOps Fundamentals
- vLLM and LLM Inference Serving (planned)
Responsible AI
Importance: Very important (4 of 5)
Ensures AI systems are fair, transparent, accountable, and compliant with governance requirements.
Course coverage
Learning path
Relevant courses
Core foundations
The concepts this role is built on.
Supporting knowledge
Adjacent knowledge that improves day-to-day judgment.
- Agent Interoperability Protocols
- Agent Loop Engineering
- AI Agent Orchestration
- AI Red Teaming
- Context Engineering
- Introduction to SwampFree
- Machine Learning Model Monitoring
- Machine Learning Model Serving
- MLOps Fundamentals
- Responsible AI
- Transformer Models
- Vector Databases
- vLLM and LLM Inference Serving (planned)
Specializations
Deeper paths for particular environments or directions.
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