Cloud AI Infrastructure Specialist

5 days ago

Vadodara, Gujarat, India beBeeCloudAIArchitect Full time ₹ 2,00,00,000 - ₹ 2,50,00,000
Job Summary:

The Cloud AI Architect is responsible for designing and implementing cloud-based AI infrastructure, integrating AI models into existing cloud applications, and ensuring alignment with business strategy.

Key Responsibilities:

  • Designing AI-powered cloud infrastructures, selecting appropriate cloud services, and ensuring scalability and performance
  • Integrating AI models into existing cloud infrastructure and applications
  • Ensuring alignment with business strategy by 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

Requirements:

  • 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
  • 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

Desirable Qualifications:

  • Advanced AI/ML certifications (Microsoft Certified: Azure AI Engineer, AWS Certified Machine Learning Specialty, Google Professional Machine Learning Engineer)
  • Experience with cutting-edge AI technologies like NLP, computer vision, reinforcement learning
  • Knowledge of emerging AI regulations and ethical AI standards
  • Experience with container orchestration (Kubernetes) for scalable AI deployments
  • Experience in hybrid and multi-cloud AI deployments

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 (e.g., GDPR, CCPA)
  • 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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