Manager-GTS AI Engg

Roles & responsibilities
Here are some of the key responsibilities of AI architect: 
Work on the Implementation and Solution delivery of the AI applications leading the team across onshore/offshore and should be able to cross-collaborate across all the AI streams.
Design end-to-end AI applications, ensuring integration across multiple commercial and open-source tools.
Work closely with business analysts and domain experts to translate business objectives into technical requirements and AI-driven solutions and applications. Partner with product management to design agile project roadmaps, aligning technical strategy. Work along with data engineering teams to ensure smooth data flows, quality, and governance across data sources.
Lead the design and implementations of reference architectures, roadmaps, and best practices for AI applications.
Fast adaptability with the emerging technologies and methodologies, recommending proven innovations.
Identify and define system components such as data ingestion pipelines, model training environments, continuous integration/continuous deployment (CI/CD) frameworks, and monitoring systems. 
Utilize containerization (Docker, Kubernetes) and cloud services to streamline the deployment and scaling of AI systems. Implement robust versioning, rollback, and monitoring mechanisms that ensure system stability, reliability, and performance.
Ensure the implementation supports scalability, reliability, maintainability, and security best practices. 
Project Management: You will oversee the planning, execution, and delivery of AI and ML applications, ensuring that they are completed within budget and timeline constraints. This includes project management defining project goals, allocating resources, and managing risks.
Oversee the lifecycle of AI application development—from design to development, testing, deployment, and optimization. 
Enforce security best practices during each phase of development, with a focus on data privacy, user security, and risk mitigation.
Provide mentorship to engineering teams and foster a culture of continuous learning. 
Lead technical knowledge-sharing sessions and workshops to keep teams up-to-date on the latest advances in generative AI and architectural best practices.

Roles & responsibilities
Here are some of the key responsibilities of AI architect: 
Work on the Implementation and Solution delivery of the AI applications leading the team across onshore/offshore and should be able to cross-collaborate across all the AI streams.
Design end-to-end AI applications, ensuring integration across multiple commercial and open-source tools.
Work closely with business analysts and domain experts to translate business objectives into technical requirements and AI-driven solutions and applications. Partner with product management to design agile project roadmaps, aligning technical strategy. Work along with data engineering teams to ensure smooth data flows, quality, and governance across data sources.
Lead the design and implementations of reference architectures, roadmaps, and best practices for AI applications.
Fast adaptability with the emerging technologies and methodologies, recommending proven innovations.
Identify and define system components such as data ingestion pipelines, model training environments, continuous integration/continuous deployment (CI/CD) frameworks, and monitoring systems. 
Utilize containerization (Docker, Kubernetes) and cloud services to streamline the deployment and scaling of AI systems. Implement robust versioning, rollback, and monitoring mechanisms that ensure system stability, reliability, and performance.
Ensure the implementation supports scalability, reliability, maintainability, and security best practices. 
Project Management: You will oversee the planning, execution, and delivery of AI and ML applications, ensuring that they are completed within budget and timeline constraints. This includes project management defining project goals, allocating resources, and managing risks.
Oversee the lifecycle of AI application development—from design to development, testing, deployment, and optimization. 
Enforce security best practices during each phase of development, with a focus on data privacy, user security, and risk mitigation.
Provide mentorship to engineering teams and foster a culture of continuous learning. 
Lead technical knowledge-sharing sessions and workshops to keep tea…