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

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.