Microsoft Azure AI Engineer — AI-102 Aligned
capability building,
designed for your organisation.
A custom-built corporate programme for software engineers, ML engineers, cloud engineers, solution architects, and senior developers (3+ years) building AI-powered applications on Microsoft Azure with Azure AI Services, Azure OpenAI, and Azure AI Foundry. We design the curriculum around your tech stack, project archetypes, and target business outcomes — delivered by domain-expert trainers and reinforced through AI-evaluated assessments.
A modular syllabus, built to be tailored.
Below is our reference curriculum. Every syllabus we deliver is tailored to your customer-specific requirements — module depth, sequencing, lab environments, and capstone projects are adapted to your team's starting point, tech stack, and target outcomes.
- Azure AI Services portfolio: Vision, Language, Speech, Document Intelligence, Translator
- Azure OpenAI Service vs. Azure AI Foundry vs. Azure ML — when each
- AI-102 exam structure: domains, weights, question patterns
- Pre-assessment: where each learner is on the 6 AI-102 domains
Want the full module-by-module syllabus, sample assignments, and pricing?
One PDF — sent to your inbox in under a minute.
Demonstrable skills your team will apply on live projects.
Architect production AI on Azure
Azure OpenAI + AI Foundry + AI Search + Cognitive Services with proper identity, networking, observability, and cost governance.
Build agents on Azure AI Foundry
Multi-agent flows on Foundry with proper evaluation, content safety, and deployment governance.
Implement enterprise RAG on Azure AI Search
Vector + hybrid + semantic ranking with Azure AI Search; multi-tenant security; index lifecycle.
Pass Microsoft AI-102 exam
Cohort first-attempt pass rate 88%.
Ship Azure AI capstone
Production-grade Azure AI application with full observability and cost governance.
Move into Azure AI Engineer role
Equipped for Azure AI Engineer Associate role at IT services firms, Microsoft partners, and enterprise IT teams.
Where your team is now vs where they'll be after the programme.
Where most teams start
- ·Familiar with Azure fundamentals (AZ-900) but no hands-on AI service experience
- ·Limited working knowledge of Azure OpenAI, Azure AI Foundry, Azure AI Search
- ·Cannot architect a production AI application on Azure with proper identity, networking, and observability
- ·No AI-102 exam preparation or familiarity with the question patterns
- ·Unfamiliar with Azure-specific AI patterns: PromptFlow, Content Safety, AI Content Filtering
- ·Limited fluency with AI Foundry agents, model catalog, and deployment patterns
Where they'll arrive
- ✓Azure AI Services mastery — Vision, Language, Speech, Document Intelligence, Translator deployed in production patterns
- ✓Azure OpenAI fluency — model deployment, fine-tuning, content filtering, monitoring, cost governance
- ✓Azure AI Foundry expertise — agents, model catalog, evaluation, prompt flow, deployment management
- ✓Azure AI Search architecture — vector search, hybrid retrieval, semantic ranking for enterprise RAG
- ✓Production discipline — VNet integration, Private Endpoints, RBAC, monitoring, cost dashboards
- ✓AI-102 certified — passes Microsoft Certified: Azure AI Engineer Associate exam
Built for L&D outcomes, not seat counts.
Prompt discipline, not prompt luck
Learners move from trial-and-error prompting to named patterns such as role prompting, few-shot, prompt chaining, and self-critique.
Reusable team assets
The programme produces Custom GPTs, reusable workflow templates, and a shared prompt library that teams can govern and scale.
Daily productivity workflows
Labs focus on email, reports, slides, meetings, spreadsheets, research synthesis, and role-based business assignments.
Measured time savings
Capstone workflows document recurring task compression, review-cycle reduction, and before/after productivity improvements.
Responsible enterprise use
Learners practise confidentiality, IP, bias detection, verification checklists, and safe-use protocols before adoption at scale.
Sustainment built in
30-day, 60-day, and 90-day check-ins help learners keep pace as ChatGPT features and frontier models evolve.
A four-milestone path from skill gap to client-ready.
Foundation & baseline
Establish a working mental model of ChatGPT, frontier models, tokens, context windows, hallucination risks, and model-selection trade-offs.
Prompt engineering labs
Learners practise CRISPE, SPEAR, role prompting, constraint-led prompting, few-shot prompting, self-critique, and prompt iteration on real work scenarios.
Custom GPTs & workflow automation
Each learner builds reusable GPTs and connects ChatGPT to productivity tools for email, documents, spreadsheets, meetings, and research workflows.
Capstone & sustainment
Learners demonstrate a personal AI productivity system and continue with prompt-of-the-week, model-of-the-month, and 30/60/90-day check-ins.
Want this curriculum aligned to your tech stack and project archetypes?
Why enterprise teams choose the B2B engagement model.
Domain-expert trainers, not professional presenters.
"My job isn't to teach ChatGPT as a tool — it's to help professionals build repeatable AI workflows, verify the output, and reclaim hours from routine work."
Taught by people who've actually shipped the work.
Built for L&D leaders and their learners.
Who this is for
- ·Knowledge workers who want to apply ChatGPT productively in their daily workflows
- ·Business analysts, consultants, marketing professionals, project managers, and individual contributors
- ·Teams that use ChatGPT for occasional drafting but need reliable, business-grade outputs
- ·Managers looking to establish team-wide prompt standards and safe-use protocols
- ·Organisations that want to automate repetitive work across email, spreadsheets, calendars, and documents
Pre-requisites
- ·No coding prerequisite for business and productivity tracks
- ·Basic familiarity with workplace tools such as email, documents, spreadsheets, slides, and meetings
- ·Willingness to bring real recurring tasks into labs for workflow redesign
- ·Enterprise cohorts should align data-handling expectations before learners use company or client information
Trusted by L&D leaders across the world.
"The programme moved our team from random prompting to a repeatable method. The prompt library and Custom GPTs became assets we could actually reuse."
"The most useful part was workflow automation. Learners took their weekly reports, meeting recaps, and research tasks and reduced hours of repetitive effort."
"Responsible use was handled practically. The team finally understood what can be pasted, what must be masked, and how to verify output before sending it."
Questions L&D teams ask before signing.
Microsoft does not publicly publish the official AI-102 exam pass rate. What Microsoft does publish is the passing score: candidates need a scaled score of 700 or higher out of 1,000 to pass Microsoft technical certification exams. Also note that AI-102 and the Azure AI Engineer Associate certification are scheduled to retire on 30 June 2026.