An Azure AI Engineer works with Microsoft's Azure cloud platform, employing artificial intelligence and machine learning techniques to design, implement, and maintain AI-driven solutions for diverse industries.
Potential Lateral Jobs
Azure AI Engineer
$120,300 / year
The average salary for Azure AI Engineer is $120,300 / year according to Glassdoor.com
There are no updated reports for Azure AI Engineer salaries. You can check potential lateral job opportunities in this information stack to find related salary information.
Azure AI Engineer role may have an alternate title depending on the company. To find more information, you can check Glassdoor.com.
As an Azure AI Engineer, you will be responsible for developing and implementing artificial intelligence solutions on the Azure platform. You will need a strong understanding of AI principles and technologies, as well as experience with programming languages like Python or TensorFlow. Strong problem-solving and analytical skills are essential, as you will be responsible for developing innovative AI solutions to meet business requirements.
The following text about the Job role of Azure AI Engineer has been generated by an AI model developed by Cohere. While efforts have been made to ensure the accuracy and coherence of the content, there is a possibility that the model may produce hallucinated or incorrect information. Therefore, we strongly recommend independently verifying any information provided in this text before making any decisions or taking any actions based on it.
The Azure AI Engineer is a technical role that involves working with artificial intelligence (AI) and machine learning (ML) technologies on the Microsoft Azure cloud platform. The engineer is responsible for designing, building, and maintaining AI and ML solutions that can be used to solve business problems and improve processes.
The most important skills for an Azure AI Engineer include:
A strong understanding of AI and ML technologies, including deep learning, neural networks, and natural language processing.
Proficiency in programming languages such as Python, R, and C++.
Experience with data science tools such as TensorFlow, PyTorch, and scikit-learn.
Knowledge of cloud computing platforms such as Azure, AWS, and Google Cloud.
Strong problem-solving and analytical skills.
Excellent communication and collaboration abilities.
The Azure AI Engineer's tasks include:
Designing and implementing AI and ML solutions on the Azure platform.
Collecting, preparing, and analyzing data for use in AI and ML models.
Building and training AI and ML models using the Azure platform.
Deploying and testing AI and ML solutions in a production environment.
Maintaining and updating AI and ML solutions to ensure they are accurate and up-to-date.
Collaborating with business and technical teams to understand business problems and develop solutions.
Conducting code reviews and providing feedback to developers.
Documenting AI and ML solutions for future reference.
The Azure AI Engineer is a crucial member of the AI and ML team, and is responsible for ensuring that AI and ML solutions are accurate, reliable, and up-to-date. They must also be able to communicate effectively with business and technical teams to ensure that solutions are aligned with business goals and objectives.
Potential Lateral Jobs
Explore the wide range of potential lateral job opportunities and career paths that are available in this role.
Most roles require at least a bachelor's degree. To remain competitive, job seekers should consider specialization or skill-specific programs such as specialization, bootcamps or certifications.
Consider pursuing specialized certifications or vendor-specific programs to enhance your qualifications and stand out in the job market.
Microsoft Certified: Azure AI Engineer Associate
The Azure AI Certification is a high-ROI program designed for professionals who are passionate about building, managing, and deploying AI solutions using Azure Cognitive Services and Azure services.
The AWS Certified Machine Learning - Specialty certification covers a wide range of topics, including data engineering, exploratory data analysis, modeling, and machine learning implementation and operations on the AWS Cloud.
Microsoft Certified: Azure Data Scientist Associate
The Microsoft Certified: Azure Data Scientist Associate is a high-ROI program designed for professionals who have expertise in applying data science and machine learning techniques to implement and manage machine learning workloads on Azure.
The AWS Data Analytics Certification program validates a deep understanding of AWS data analytics services and their integration with each other to derive insights from data, making it suitable for individuals pursuing a role focused on data analytics.
If you want to improve your skills and knowledge in a particular field, you should think about enrolling in a Nanodegree or specialization program. This can greatly improve your chances of finding a job and make you more competitive in the job market.
AI Engineer using Microsoft Azure
You'll master the skills to implement machine learning models, design and build end-to-end AI solutions using Azure Cognitive Services, and deploy, monitor, and manage continuous improvement of Azure AI solutions, making you an ideal candidate for Microsoft certification A1-102.
We are soon crowdsourcing these resource stacks to collate the best resources, such as publications, community groups, job boards, etc., that are practically suitable for every contextual stack.
Discover the wide array of publications that professionals in this role actively engage with, expanding their knowledge and staying informed about the latest industry trends and developments.
Discover the thriving communities where professionals in this role come together to exchange knowledge, foster collaboration, and stay at the forefront of industry trends.
We are currently in the process of updating contextual resources and we will be adding the new ones to the list shortly.
AI Disclosure: We are testing AI technologies to ensure the accuracy and coherence of recommendations. However, it is important to note that there is a possibility that the model may create hallucinated or incorrect inferences. Therefore, we highly recommend independently verifying any information provided in these stacks before making any decisions or taking any actions based on it.
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