Chief AI Infrastructure Specialist

2 days ago

Thāne, Maharashtra, India beBeeAI Full time ₹ 1,50,00,000 - ₹ 2,50,00,000
Cloud AI Architect

Greetings from a leading organization in the field of technology.

We are hiring for a Cloud AI Architect to design and deploy scalable, secure, and efficient cloud-based AI solutions.

  • The ideal candidate will have strong knowledge of AI/ML frameworks (TensorFlow, PyTorch, scikit-learn) integrated with cloud platforms.

Required Technical Skills:

  • 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.

Responsibilities:

  • Designing AI-powered cloud infrastructures: Selecting appropriate cloud services, designing the architecture, 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 translating 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.
  • Leveraging serverless AI services and edge AI for real-time, scalable applications.
  • Collaborating on AI governance and compliance with evolving data privacy laws.
  • Utilizing multi-cloud AI strategies and hybrid-cloud integration for flexibility and disaster recovery.
  • Driving cost optimization of AI workloads via cloud resource tuning and autoscaling.

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