Cloud AI Architect (Azure / AWS / Google)

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

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.