Cloud AI Infrastructure Specialist

1 week ago

Kannur, Kerala, India beBeeCloudAI Full time ₹ 1,50,00,000 - ₹ 2,50,00,000

Greetings from TCS

TCS is hiring for Cloud AI Architect

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

Required Technical Skill Set

We are seeking an innovative and experienced architect who can design cloud-based infrastructures that incorporate AI models seamlessly.

The ideal candidate will have deep knowledge of AI/ML frameworks, cloud services, and programming skills in Python, R, and Java.

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

  • Proficient in designing and implementing AI-powered cloud infrastructures.
  • Expertise in integrating AI models into existing cloud infrastructure and applications.
  • Familiarity with cloud-native MLOps pipelines for continuous training, deployment, and monitoring.

Good-to-Have

  • Advanced AI/ML certifications.
  • Experience with cutting-edge AI technologies like NLP, computer vision, reinforcement learning.
  • Knowledge of emerging AI regulations and ethical AI standards.

Responsibility of / Expectations from the Role

Designing AI-powered cloud infrastructures: Selecting appropriate cloud services, designing architecture, 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.

Maintenance and improvement of AI solutions: Ensuring that AI solutions are maintained and improved continuously to meet changing business needs.

Foster a culture of innovation and experimentation: Encouraging creativity and calculated risk-taking within the team.

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