HCL Technologies
Technical Project Lead
HCL Technologies, Seattle, Washington, us, 98127
Job Description (Posting).
Job Description: Datacenter ArchitectKey Responsibilities:
Architecture Design:
Deep understanding of comprehensive datacenter architecture plans that align with business goals and technical requirements.Deep understanding of scalable, secure, and efficient datacenter infrastructures.Good knowledge in high availability and disaster recovery capabilities.
Physical Build and Deployment:
Understanding of physical build and deployment of datacenter infrastructure, including servers, storage, networking, and virtualization.Understanding of installation, configuration, and maintenance of datacenter hardware and software.
Logical Build and Deployment:
Deep understanding in design and implementation of logical datacenter architectures using tools such as Azure DevOps.Good understanding and working knowledge in CI/CD pipelines for automated deployment and configuration.Good understanding in seamless integration of various components and services within the datacenter.
Automation:
Ability to strategize and implement automation to streamline datacenter operations.Good understanding in tools such as Ansible, Puppet, or Chef for configuration management and automation.Ability to think through scenarios for scripts and automation workflows to reduce manual intervention and improve efficiency.
AI Solutions:
Good understanding to Integrate AI and machine learning solutions to enhance datacenter performance and reliability.Good understanding around predictive analytics models to foresee and mitigate potential issues.Good knowledge in AI-driven monitoring and management tools to optimize resource utilization.
SILICON Area:
Good knowledge in designing and managing high-performance computing (HPC) environments tailored for AI and machine learning workloads.Knowledge in datacenter infrastructure for specialized hardware such as GPUs, TPUs, and FPGAs.Knowledge in efficient cooling and power management for high-density compute environments.
AI Datacenters:
Knowledge in datacenters specifically for AI workloads, focusing on data throughput, latency, and scalability.Knowledge in AI-driven optimization techniques for resource allocation and workload management.Ability to collaborate with AI research and development teams to support cutting-edge AI applications and services.
Qualifications:
Bachelor's degree in computer science, Information Technology, or a related field.Proven experience as a Datacenter Architect or similar role.Strong knowledge of datacenter infrastructure, including servers, storage, networking, and virtualization.Proficiency in automation tools and scripting languages (e.g., Python, PowerShell).Experience with AI and machine learning applications in datacenter environments.Excellent problem-solving skills and attention to detail.Strong communication and collaboration skills.Preferred Skills:
Certifications such as Cisco Certified Network Professional (CCNP), VMware Certified Professional (VCP), or similar.Experience with cloud platforms (e.g., AWS, Azure, Google Cloud).Knowledge of containerization technologies (e.g., Docker, Kubernetes).
Job Description: Datacenter ArchitectKey Responsibilities:
Architecture Design:
Deep understanding of comprehensive datacenter architecture plans that align with business goals and technical requirements.Deep understanding of scalable, secure, and efficient datacenter infrastructures.Good knowledge in high availability and disaster recovery capabilities.
Physical Build and Deployment:
Understanding of physical build and deployment of datacenter infrastructure, including servers, storage, networking, and virtualization.Understanding of installation, configuration, and maintenance of datacenter hardware and software.
Logical Build and Deployment:
Deep understanding in design and implementation of logical datacenter architectures using tools such as Azure DevOps.Good understanding and working knowledge in CI/CD pipelines for automated deployment and configuration.Good understanding in seamless integration of various components and services within the datacenter.
Automation:
Ability to strategize and implement automation to streamline datacenter operations.Good understanding in tools such as Ansible, Puppet, or Chef for configuration management and automation.Ability to think through scenarios for scripts and automation workflows to reduce manual intervention and improve efficiency.
AI Solutions:
Good understanding to Integrate AI and machine learning solutions to enhance datacenter performance and reliability.Good understanding around predictive analytics models to foresee and mitigate potential issues.Good knowledge in AI-driven monitoring and management tools to optimize resource utilization.
SILICON Area:
Good knowledge in designing and managing high-performance computing (HPC) environments tailored for AI and machine learning workloads.Knowledge in datacenter infrastructure for specialized hardware such as GPUs, TPUs, and FPGAs.Knowledge in efficient cooling and power management for high-density compute environments.
AI Datacenters:
Knowledge in datacenters specifically for AI workloads, focusing on data throughput, latency, and scalability.Knowledge in AI-driven optimization techniques for resource allocation and workload management.Ability to collaborate with AI research and development teams to support cutting-edge AI applications and services.
Qualifications:
Bachelor's degree in computer science, Information Technology, or a related field.Proven experience as a Datacenter Architect or similar role.Strong knowledge of datacenter infrastructure, including servers, storage, networking, and virtualization.Proficiency in automation tools and scripting languages (e.g., Python, PowerShell).Experience with AI and machine learning applications in datacenter environments.Excellent problem-solving skills and attention to detail.Strong communication and collaboration skills.Preferred Skills:
Certifications such as Cisco Certified Network Professional (CCNP), VMware Certified Professional (VCP), or similar.Experience with cloud platforms (e.g., AWS, Azure, Google Cloud).Knowledge of containerization technologies (e.g., Docker, Kubernetes).