Capacity Operations Lead - Remote at Jobgether

Great Sankey, England, United Kingdom -

Full Time

Start Date

Immediate

Expiry Date

19 Jun, 26

Salary

0.0

Posted On

21 Mar, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

HPC Cluster Development, GPU Capacity Management, Cloud Platforms, Resource Optimization, Data Models, Reporting Platforms, Performance Measures, Technical Requirements Assessment, Performance Bottleneck Identification, Efficiency Initiatives, Cloud Infrastructure Tooling, Analytics, Cloud Architecture, Data Management, AI Tools, Statistical Modeling

Industry

Internet Marketplace Platforms

Description

This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Capacity Operations Manager - REMOTE. In this role, you will play a pivotal part in coordinating the development of High-Performance Computing (HPC) clusters, enhancing GPU capacity management, and optimizing resources across various cloud platforms. Your insights will guide strategic capacity decisions while implementing innovative technologies that advance not only our cloud infrastructure but also the overall user experience. Collaborating with cross-functional teams will be essential to drive efficiency initiatives, ensuring that our advancements are both impactful and sustainable. Join us to make a significant contribution to the future of computing technology. \n Accountabilities Coordinate the development of High Performance Computing (HPC) clusters. Direct and improve GPU capacity and compute resources across cloud service platforms. Design and manage data models, reporting platforms, and performance measures. Assess technical and business requirements for GPU capacity. Identify performance bottlenecks in compute resource usage. Drive efficiency initiatives in partnership with engineering and finance teams. Develop and enhance tooling for cloud infrastructure and analytics. Collaborate with Finance, Product, and Infrastructure Engineering teams. Requirements Bachelor's or Master's degree in Computer Science, Software Engineering, or related field. 8+ years of experience in cloud computing and managing GPU capacity. Strong technical proficiency in cloud architecture and data management. Comprehensive knowledge of cloud service models (IaaS, PaaS, SaaS). Experience with cloud service providers like AWS, Azure, GCP. Demonstrated experience using AI tools for data insights. Knowledge of statistical modeling and machine learning methodologies. Strong communication and relationship-building skills. Self-starter with a focus on continuous learning and adaptation. Ability to operate effectively in uncertain and rapidly changing environments. Benefits Competitive salary based on location and experience. Equity options available. Comprehensive health and wellness benefits. Opportunities for professional growth and development. Flexible working hours and remote work option. Dynamic and inclusive work environment. Access to cutting-edge technology and resources. \n Why Apply Through Jobgether? We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best! Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time. #LI-CL1

Responsibilities

The role involves coordinating the development of High-Performance Computing (HPC) clusters and enhancing GPU capacity management across various cloud platforms. Key accountabilities include designing data models, assessing technical requirements, identifying performance bottlenecks, and driving efficiency initiatives.