Azure AI Engineer

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Azure AI Engineer

On Boarding Process

  • 1
    Job description

    Providing a clear and comprehensive overview of the role's responsibilities and expectations.

  • 2
    Curated Talent Shortlisting

    Thoughtfully selecting and presenting a tailored pool of skilled candidates.

  • 3
    In-depth Consultant Engagement

    Engaging candidates in thorough discussions to understand their expertise and alignment with client needs.

  • 4
    Seamless Integration into Client Team

    Ensuring a smooth transition and effective collaboration as the consultant becomes an integral part of the client's team.

Scouting for Azure AI Engineer?

An Azure AI Engineer specializes in the design, implementation, and deployment of AI solutions on the Microsoft Azure platform. They collaborate with data scientists, data engineers, and other stakeholders to bring AI-driven applications to fruition in an efficient and scalable manner.

An Azure AI Engineer plays a pivotal role in harnessing the power of artificial intelligence to solve real-world problems, ensuring that solutions are robust, scalable, and efficient on the Azure platform.

Anicalls's consultants are Specializes in building, training, and deploying machine learning models using Azure's suite of AI tools and services.

Roles & Responsibilities

Solution Design & Architecture

Design and implement end-to-end AI solutions using Azure Machine Learning Service, Azure Databricks, and other Azure AI services. Architect AI applications ensuring scalability, efficiency, and data security.

Model Development & Training

Collaborate with data scientists to implement and train machine learning models using Azure ML. Optimize model training processes using Azure's distributed and GPU capabilities.

Model Deployment & Management

Deploy machine learning models as web services on Azure Kubernetes Service or Azure Container Instances. Monitor and manage the performance and lifecycle of deployed models using Azure ML Model Management.

Integration with Azure Services

Integrate AI solutions with other Azure services such as Azure IoT Hub, Azure Stream Analytics, and Cosmos DB for real-time analytics. Utilize Azure Cognitive Services to incorporate pre-built AI functionalities like vision and language processing.

Data Engineering

Work with data engineers to ensure data is ingested, cleaned, and transformed efficiently using Azure Data Factory or Azure Databricks. Implement real-time data streaming solutions using Azure Event Hub or Azure IoT Hub.

Monitoring & Optimization

Monitor AI applications' performance using Azure Application Insights or Azure Log Analytics. Continuously optimize models, data pipelines, and compute resources for better performance and cost-efficiency.

Security & Compliance

Implement security measures for AI solutions, including data encryption, network security, and access controls. Ensure AI applications adhere to regulatory and compliance requirements.

Continuous Learning & Research

Stay updated on the latest developments in AI, machine learning, and Azure services. Prototype and experiment with new algorithms, tools, and best practices.

Documentation & Best Practices

Document the architecture, design, and best practices for AI solutions. Mentor and guide junior engineers and other team members in AI best practices and tools.

Benefits With Anicalls

Our Commitments

Guaranteed Talent Satisfaction

Flexible Engagement Terms

Performance Review Report Policy


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