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Tai Labs Certified AI Engineer

TAI-AIE

Designs, evaluates and deploys production-minded AI applications with retrieval, tools, agents, guardrails and cost control.

Effort
12 weeks
Level
Professional
Passing
75%
Earn this certificationVerify a credential
Tai Labs Certified AI Engineer — Code lattice, API nodes, model gateway and deployed application signalTAI-AIEFlagship professional certificationAI Engineering

Competency blueprint

Assessment weighting is published up front — 20% knowledge, 30% labs, 20% portfolio and 30% capstone.

  • Model interfaces

    20%

    APIs, structured outputs and tool calling

  • Retrieval

    25%

    Embeddings, vector stores and RAG quality

  • Agents and tools

    20%

    Agent loops, MCP and external systems

  • Evaluation and guardrails

    20%

    Eval suites, failure analysis and safety controls

  • Deployment

    15%

    Monitoring, latency and cost in production

Assessment blueprint

Knowledge

20%

Labs

30%

Portfolio

20%

Capstone

30%

75% overall to pass. The capstone must pass independently of every other score. Two retakes per assessment window; capstone may be revised twice within 90 days.

Capstone

Build and deploy a functioning production-minded AI application.

Modules

  • LLM and generative-AI fundamentals
  • Python for AI
  • OpenAI, Anthropic and Gemini APIs
  • Structured outputs and tool calling
  • Embeddings and vector databases
  • RAG
  • Agents
  • MCP and external tools
  • Evaluations and guardrails
  • Deployment, monitoring and cost