We are looking for a highly qualified Azure AI Data Platform Architect to join our team.

Using AI and machine learning to support data-driven decision-making, you will be in charge of planning, creating, and deploying scalable data solutions on the Azure platform. In order to develop strong data architectures that complement our clients' strategic goals, you will collaborate closely with cross-functional teams made up of data scientists, data engineers, and business stakeholders.

Azure AI Data Platform Architect || 14-18 Y || 100% Remote || Quick Joiner ||

Key Responsibilities:

Develop and install Azure-based data platforms to support AI and machine learning activities.

To guarantee the smooth integration of data pipelines and AI models, cooperate with the data science and data engineering teams.

Create best practices, standards, and data architecture frameworks for data management and governance.

Examine and improve current procedures and data architectures to increase scalability and performance.

Find ways to increase corporate value by utilizing AI and machine learning technologies.

Team members should receive technical leadership and direction on Azure data services, AI technologies, and best practices.

Perform proof-of-concept initiatives to assess novel instruments, technologies, and approaches.

Work together with customers to comprehend their business needs and convert them into technical solutions.

Keep abreast on market developments and new developments in cloud computing, AI, and data platforms.

Required Skills and Experience: (13-18 Years)

Bachelor's degree in computer science, data science, engineering, or a related discipline; a master's degree is recommended.

Expertise as a data platform architect, specializing on AI and Azure data services.

Strong familiarity with Azure data services, including Azure Machine Learning, Azure Data Lake, Azure Synapse Analytics, and Azure Databricks.

Familiarity with data warehousing principles, ETL procedures, and data modeling.

Knowledge of programming languages for data analysis and manipulation, such as Python, R, or Scala.

Familiarity with best practices for data governance, security, and compliance.

Outstanding problem-solving abilities and the capacity to operate in a fast-paced, team-oriented setting.

Excellent communication abilities, including the capacity to explain intricate technical ideas to stakeholders who are not technically inclined.