| name | azure-ai-engineer |
| description | Azure AI Engineer Associate (AI-102) grounded skill. Covers AI solution planning, Document Intelligence, Azure AI Search, Azure OpenAI/Foundry integration, custom vision, NLP pipelines, and conversational AI. Use when building or reviewing AI service integrations. Triggers on: AI-102, AI Search, Document Intelligence, Foundry SDK, semantic search, vector search, RAG, custom models, AI pipeline, knowledge mining.
|
| version | 1.0.0 |
| updated | 2026-04-18 |
| scope | repo |
| certification_source | AI-102: Microsoft Azure AI Engineer Associate |
| learn_path | https://learn.microsoft.com/en-us/credentials/certifications/azure-ai-engineer/ |
| feeds_scoring | Solutions Partner Data & AI — Skilling metric (+4 pts) |
Azure AI Engineer Associate (AI-102) — Agent Skill
You are grounded in the AI-102 certification knowledge domain. Use mcp__microsoft-learn__microsoft_docs_search and mcp__microsoft-learn__microsoft_docs_fetch for real-time grounding.
When to activate
- Building or reviewing AI Search indexes (
srch-ipai-dev-sea)
- Working with Document Intelligence models (
docai-ipai-dev)
- Integrating Foundry/OpenAI APIs (
ipai-copilot-resource)
- Designing RAG pipelines or knowledge mining
- Building custom AI solutions with Azure AI Services SDK
Knowledge domains (AI-102 exam skills)
1. Plan and manage an AI solution (15-20%)
- Select appropriate Azure AI services for a solution
- Plan and configure security (MI auth, Key Vault, network isolation)
- Create and manage an AI resource (Foundry resource, project)
IPAI mapping:
ipai-copilot-resource → Foundry resource (EUS2, S0)
└── ipai-copilot → Foundry project (Failed provisioning — retry via portal)
docai-ipai-dev → Document Intelligence (SEA)
srch-ipai-dev-sea → AI Search with PE (SEA)
Auth: DefaultAzureCredential → id-ipai-dev MI
Secrets: kv-ipai-dev-sea (PE-only access)
2. Implement Document Intelligence solutions (10-15%)
- Analyze documents with prebuilt models
- Build custom extraction models
- Implement form processing pipelines
IPAI implementation:
ipai_doc_intel Odoo module — prebuilt-invoice model (26 fields, 89-97% confidence)- Custom TBWA models planned (CA form + Expense Report) — R3-S11
- SDK:
azure-ai-formrecognizer → azure-ai-documentintelligence
3. Implement Azure AI Search solutions (15-20%)
- Create and manage search indexes
- Implement semantic ranking and vector search
- Build knowledge mining pipelines with skillsets
IPAI implementation:
srch-ipai-dev-sea with private endpoint- Foundry IQ uses AI Search for citation-backed grounding
- Vector search for Pulser knowledge base (planned R2)
4. Implement Azure OpenAI / Foundry solutions (15-20%)
- Deploy and manage model deployments
- Implement chat completions and embeddings
- Use Responses API (Agents v2)
- Implement RAG patterns
IPAI implementation:
from azure.ai.projects import AIProjectClient
from azure.identity import DefaultAzureCredential
client = AIProjectClient(
endpoint="https://ipai-foundry-sea.services.ai.azure.com/api/projects/ipai-copilot",
credential=DefaultAzureCredential(),
)
agent = client.agents.create_agent(model="gpt-4.1-mini", ...)
5. Implement NLP solutions (15-20%)
- Analyze text (sentiment, entities, key phrases, PII)
- Build conversational language understanding models
- Implement custom text classification
IPAI application: Pulser NLP for finance — extract tax codes from invoices, classify BIR form types, generate plain-language close summaries.
6. Implement knowledge mining and cognitive search (10-15%)
- Design skillset pipelines
- Implement incremental enrichment
- Build custom skills
IPAI application: Odoo data → AI Search index → Foundry IQ grounding → Pulser citation-backed responses.
Grounding rule
Before answering any AI-102 domain question:
mcp__microsoft-learn__microsoft_docs_search(query="<topic> Azure AI engineer")
For implementation details, follow up with:
mcp__microsoft-learn__microsoft_docs_fetch(url="<specific doc URL from search>")
Then apply IPAI-specific context (resource names, endpoints, auth patterns).
