A good AI engineer is skilled at designing innovative AI solutions and collaborating across teams. Key skills include:

• Programming: Proficiency in Python or similar languages.

• Machine Learning: Expertise in programming frameworks. 

• Data Analysis: Strong skills in handling and interpreting large datasets. 

• Cloud Platforms: Experience with Azure or AWS. 

• Problem-Solving: Creative and analytical thinking for AI challenges. 

• Adaptability: Commitment to continuous learning in a rapidly evolving field. 

This blend of skills allows them to bridge the gap between AI needs and technological solutions, driving successful AI projects. 

Lumify Learn’s Certified AI Engineer Professional course covers all the technical and practical skills required in an AI Engineer role. These areas include (but are not limited to): AZ-900: Microsoft Azure Fundamentals Foundational knowledge of cloud services and how Microsoft Azure provides these services. Key skills include: 

• Understanding cloud concepts, such as High Availability, Scalability, Elasticity, Agility, and Disaster Recovery. 

• Comprehending core Azure services, including architectural components like regions, availability zones, and resource groups. 

• Grasping security, privacy, compliance, and trust within Azure. 

• Understanding Azure pricing, Service Level Agreements (SLAs), and lifecycle management. 

AI-900: Microsoft Azure AI Fundamentals: Basic concepts of artificial intelligence (AI) and the services in Microsoft Azure that can be used to create AI solutions. Key skills include: 

• Understanding AI workloads and considerations, such as identifying features of common AI workloads and guiding principles for responsible AI. 

• Understanding fundamental principles of machine learning on Azure, including common machine learning types and core machine learning concepts. 

• Understanding features of computer vision workloads on Azure, like image analysis, object detection, and facial recognition. 

• Understanding features of Natural Language Processing (NLP) workloads on Azure, including text analytics, language understanding, and translation. 

• Understanding features of conversational AI workloads on Azure, such as the Azure Bot Service. 

Essential AI Programming Knowledge: Provides a strong foundation and hands-on experience with Python and C# skills that will be useful to become an AI engineer. 

•  Programming Language of Python and C# 

• Learn to integrate with REST APIs & JSON to integrate AI models 

• Prepare and manage datasets for machine learning 

• Fundamental AI & Machine Learning frameworks 

AI-901: Microsoft Azure AI Fundamentals: 

Basic concepts of artificial intelligence (AI) and the services in Microsoft Azure that can be used to create AI solutions. Key skills include:

• Understanding AI workloads and considerations, such as identifying features of common AI workloads and the guiding principles for responsible AI.

• Understanding fundamental principles of machine learning on Azure, including common machine learning types and core machine learning concepts.

• Understanding features of computer vision workloads on Azure, such as image classification, object detection, and facial recognition.

• Understanding features of Natural Language Processing (NLP) workloads on Azure, including text analytics, language understanding, and translation.

• Understanding features of generative AI workloads on Azure, such as large language models, prompt engineering, and the ethical use of generative AI.

• Fundamental Python Syntax.

• Implementing AI solutions using Microsoft Foundry, including how to explore, deploy, and interact with AI models in Azure.

Essential AI Programming Knowledge

Provides a strong foundation and hands-on experience with Python skills that will be useful to become an AI engineer.

• Programming Language of Python

• Learn to integrate with REST APIs & JSON to integrate AI models

• Understanding how embeddings represent text numerically, performing semantic similarity search, and building basic Retrieval-Augmented Generation (RAG) pipelines.

• Implementing managed identity and keyless credential patterns, avoiding hardcoded API keys, and applying secure authentication methods

AI-103: Microsoft Azure AI Apps and Agents Developer Associate

Designed for developers and AI engineers ready to build, deploy, and manage intelligent AI applications. Key skills include:

• Planning and Managing an Azure AI Solution: Selecting appropriate Azure AI services, configuring secure authentication, and applying responsible AI principles throughout the solution lifecycle.

• Implementing Generative AI and Agentic Solutions: Building generative AI applications, designing autonomous AI agents, implementing Retrieval-Augmented Generation (RAG), and defining tool and function calling schemas.

• Implementing Computer Vision Solutions: Analysing images, detecting objects, and extracting structured data from visual content using Azure AI Vision.

• Implementing Text Analysis Solutions: Processing and analysing text using Azure AI Language services, including sentiment analysis, entity recognition, and language understanding.

• Implementing Information Extraction Solutions: Extracting structured information from documents and unstructured data using Azure AI Document Intelligence and Azure AI Search.

• Developing with Azure AI SDKs and APIs: Writing Python applications that call Azure AI services, handle streaming responses, and integrate AI capabilities into production-ready solutions.

Completing this AI Engineer course prepares you for various roles and pathways, such as:

• Junior AI Engineer: In this role, you will develop and implement machine learning models, assists in data preparation, and supports AI system deployment.

• AI Support Specialist: Assist in troubleshooting, maintaining, and optimizing AI systems while providing technical support to users and resolving AI-related issues.

• AI Operation Specialist: Manages, monitors, and optimizes the performance of deployed AI systems to ensure seamless functionality and scalability.

• AI Solution Architect: Designs and oversees the implementation of AI systems, ensuring they align with business goals and integrate seamlessly into existing infrastructure.

The course is open to anyone interested in pursuing an AI Engineer career, regardless of prior experience. There are no specific prerequisites. This makes the program a great option for those new to the field or looking to switch careers.