Cloud Infrastructure Specialist

2 weeks ago

Agra, Uttar Pradesh, India beBeeCloudArchitect Full time ₹ 2,00,00,000 - ₹ 2,50,00,000

Designing AI-powered cloud infrastructure requires expertise in selecting appropriate cloud services, designing the architecture, and ensuring scalability and performance.

This role involves integrating AI models seamlessly into existing cloud infrastructure and applications, aligning with business strategy by working with stakeholders to understand business requirements and translate them into technical solutions.

The ideal candidate will oversee deployment process, monitor performance, and ensure the long-term health of AI solutions. Additionally, they should anticipate future business requirements and plan for the evolution of AI systems.

Required Technical Skill Set
  • Deep knowledge of AI/ML frameworks (TensorFlow, PyTorch, scikit-learn) integrated with cloud platforms.
  • Proficient with cloud AI services: Azure AI (Cognitive Services, ML Studio), AWS AI/ML (SageMaker, Rekognition), Google AI Platform.
  • Experience with MLOps tools (Kubeflow, MLflow, Azure MLOps).
  • Strong programming skills in Python, R, or Java.
  • Understanding of data engineering concepts and cloud data platforms.
  • Familiarity with cloud security standards and data privacy regulations.
Desired Qualifications
  • Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or related field.
  • 10-15 years of experience in cloud architecture with 5+ years specializing in AI/ML system design and deployment.

Responsibilities

  • Designing AI-powered cloud infrastructures: Selecting appropriate cloud services, designing the architecture, and ensuring scalability and performance.
  • Integrating AI models: Seamlessly incorporating AI models into existing cloud infrastructure and applications.
  • Ensuring alignment with business strategy: Working with stakeholders to understand business requirements and translate them into technical solutions.
  • Managing deployment and maintenance: Overseeing the deployment process, monitoring performance, and ensuring the long-term health of AI solutions.
  • Identifying future AI needs: Anticipating future business requirements and planning for the evolution of AI systems.

Additional Considerations: Designing and implementing responsible AI and ethical AI frameworks ensuring bias mitigation and fairness. Integrating AI solutions with cloud-native MLOps pipelines for continuous training, deployment, and monitoring.

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