Senior AI Cloud Infrastructure Specialist

4 days ago

Thiruvananthapuram, Kerala, India beBeeArtificialintelligence Full time ₹ 1,75,00,000 - ₹ 2,02,50,000

Greetings from our organization

We are hiring a Cloud AI Architect

This role involves designing and implementing artificial intelligence (AI) systems that integrate with cloud platforms.

The ideal candidate will have a strong background in computer science, software engineering, and data analysis, as well as experience working with machine learning frameworks and tools.

Responsibilities:

  • Designing and deploying AI-powered cloud infrastructures using cloud services such as Azure AI, AWS AI/ML, and Google AI Platform
  • Integrating AI models into existing cloud applications and infrastructure
  • Collaborating with stakeholders to understand business requirements and translate them into technical solutions
  • Managing the deployment process and monitoring performance of AI solutions

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 integrated with cloud platforms
  • Proficient with cloud AI services: Azure AI, AWS AI/ML, 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

Benefits:

  • Opportunity to work on cutting-edge AI technologies like NLP, computer vision, reinforcement learning
  • Chance to collaborate with experienced professionals in the field of AI and cloud computing
  • Flexibility to work in a hybrid or multi-cloud environment

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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