Cloud AI Architect

2 weeks ago

Hyderabad, India Tata Consultancy Services Full time
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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