Guide to Becoming an AI Architect

Steps for a career as an AI architect for people interested in artificial intelligence

An IT expert who develops and deploys AI systems for diverse networks, databases, and applications is known as an AI architect. To coordinate and grow AI activities, they link data analysts, database administrators, programmers, operators, and business leaders. Using the most recent artificial intelligence technology, they can comprehend the needs of the consumers and offer workable solutions. The design must be balanced with the limitations, such as competing demands and other restrictions. They must be highly skilled and experienced in AI-related tools and information technology, as well as able to follow industry trends and select the most appropriate technology for each project.

AI Architect Skills:

AI architects need to have a variety of skills to design and implement AI systems for different applications, databases, and networks. Some of the skills are:

Technical Skills: AI architects should know how to deal with the different aspects and trade-offs of data processing, management, simulation, deployment, and production. They should also understand the machine learning and deep learning pipelines, frameworks, and methods. They should be familiar with advanced analytics tools and applied mathematics.

Soft Skills:

• Thought Leadership: AI architects should be able to lead the change towards an AI-driven culture in the organization. They should also be able to communicate the benefits and risks of AI to the IT executives.

• Collaborative Mindset: AI architects should be able to work with data scientists, database administrators, ML engineers, other engineers, business executives, and CxOs. They should be able to coordinate and align the AI platforms with the business and technical goals.

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AI Architect Salary

The salary of an AI architect may vary depending on the company, the knowledge, and the experience of the candidate. Here are the average annual salaries of an AI architect in the US and India:

• Salary in the US: The average salary of an AI architect in the US is $181,500 per year or $93.08 per hour. An entry-level AI architect can earn $170,001 per year, while an experienced AI architect can earn up to $234,900 per year.

• Salary in India: The average salary of an AI architect in India is INR 2,750,000 per year or 1,410 per hour. An entry-level AI architect can earn between 2,250,000 and 2,880,000 rupees per year, while an experienced AI architect can earn up to 2,880,000 rupees per year.

AI Architect Job Outlook

There are many opportunities for AI architects in the market, as many renowned companies are looking for AI architects and offering attractive compensation packages. Some of the positions available at Microsoft, Amazon Web Services (AWS), and Google are:

• Conversational AI Architect: This position involves designing and implementing conversational AI solutions using natural language processing and speech technologies.

• Associate Cloud Architect: This position involves designing and developing cloud-based solutions using AWS services and best practices.

• Artificial Intelligence Solution Architect: This position involves designing and delivering AI solutions using machine learning, deep learning, computer vision, and natural language understanding.

• Data, Analytics, and AI Architect: This position involves designing and building data and analytics platforms using Azure services and tools.

• AWS Data & AI Solution Architect: This position involves designing and implementing data and AI solutions using AWS technologies such as S3, Redshift, Glue, Athena, SageMaker, and Rekognition.

• Cloud Solution Architect – Data & AI: This position involves designing and deploying data and AI solutions on the cloud using Google Cloud Platform services such as BigQuery, Dataflow, Dataproc, Cloud ML Engine, and Vision API.

• Data & Analytics – Artificial Intelligence Architect: This position involves designing and delivering data and analytics solutions using artificial intelligence techniques such as machine learning, deep learning, natural language processing, computer vision, and cognitive computing.