Cloud AI Architect

4 days ago

Hyderabad, Telangana, India Tata Consultancy Services Full time ₹ 12,00,000 - ₹ 36,00,000 per year

Greetings from TCS

TCS is hiring for Cloud AI Architect

Role
- Cloud AI Architect (Azure/AWS/Google)

Required Technical Skill Set

Cloud Services for AI/ML frameworks, Azure AI (Cognitive Services, ML Studio), AWS AI/ML (SageMaker, Rekognition), Google AI Platform. MLOps tools (Kubeflow, MLflow, Azure MLOps). programming skills in Python, R and Java.

Desired Experience Range

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.

Desired Competencies (Technical/Behavioral Competency)

Must-Have

  • 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.
  • Excellent leadership and stakeholder management skills.

Good-to-Have

  • 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.

Responsibility of / Expectations from the Role

Designing AI-powered cloud infrastructures: This includes 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.

Collaboration with various teams: Working with data scientists, data engineers, and developers to implement AI solutions.

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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