Descrizione dell'offerta
Experteer Overview
In this role you design and optimize AI/ML infrastructure across cloud and on‑prem environments. You will lead architecture workstreams, implement scalable compute and data pipelines, and drive production deployment of AI systems. You partner with cross‑functional teams to ensure secure, efficient, and compliant operations while advancing InfraOps and MLOps practices. Your work supports high‑impact, real‑world AI applications and requires hands‑on coding, deployment, and mentoring. This is a ownership‑driven opportunity to shape the AI infrastructure roadmap at scale. Retribuzione / Benefits
Write, review, and debug code, scripts, and infrastructure‑as‑code for AI infrastructure and tooling; set quality standards Architect, configure, and provision cloud and on‑prem compute resources (GPU clusters, distributed training) for performance and utilization Design and maintain deployment automation and CI/CD pipelines for reliable releases of AI systems Deploy AI systems, models, and data pipelines into production and codify best practices Lead container orchestration and model serving using Docker, Kubernetes, and deployment frameworks Architect and optimize the computational stack for performance, power, cost, and scalability Evaluate and select tools and platforms to shape the infrastructure roadmap Integrate AI models into enterprise systems ensuring interoperability, security, and compliance Own AI monitoring and infra health across InfraOps and MLOps, drive remediation Troubleshoot complex issues across hardware, networking, software, and models and perform root‑cause analysis Mentor junior engineers and lead code reviews; define architecture standards, processes, and cost‑efficient practices Responsabilità
Practical experience in coding, building, monitoring, and troubleshooting AI/ML applications and deploying/running them on premises or in public cloud Strong understanding of AI/ML concepts Strong understanding of computing infrastructure; preferred AI infra knowledge Proficiency in Python, Java, or C++ Experience with data pipeline/workflow tools (e.g., Apache Airflow, Kubeflow) Strong problem‑solving skills and fast pace Excellent communication and collaboration Proven experience in AI/ML infra engineering or related roles on a hyperscaler platform for deploying large scale solutions
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In this role you design and optimize AI/ML infrastructure across cloud and on‑prem environments. You will lead architecture workstreams, implement scalable compute and data pipelines, and drive production deployment of AI systems. You partner with cross‑functional teams to ensure secure, efficient, and compliant operations while advancing InfraOps and MLOps practices. Your work supports high‑impact, real‑world AI applications and requires hands‑on coding, deployment, and mentoring. This is a ownership‑driven opportunity to shape the AI infrastructure roadmap at scale. Retribuzione / Benefits
Write, review, and debug code, scripts, and infrastructure‑as‑code for AI infrastructure and tooling; set quality standards Architect, configure, and provision cloud and on‑prem compute resources (GPU clusters, distributed training) for performance and utilization Design and maintain deployment automation and CI/CD pipelines for reliable releases of AI systems Deploy AI systems, models, and data pipelines into production and codify best practices Lead container orchestration and model serving using Docker, Kubernetes, and deployment frameworks Architect and optimize the computational stack for performance, power, cost, and scalability Evaluate and select tools and platforms to shape the infrastructure roadmap Integrate AI models into enterprise systems ensuring interoperability, security, and compliance Own AI monitoring and infra health across InfraOps and MLOps, drive remediation Troubleshoot complex issues across hardware, networking, software, and models and perform root‑cause analysis Mentor junior engineers and lead code reviews; define architecture standards, processes, and cost‑efficient practices Responsabilità
Practical experience in coding, building, monitoring, and troubleshooting AI/ML applications and deploying/running them on premises or in public cloud Strong understanding of AI/ML concepts Strong understanding of computing infrastructure; preferred AI infra knowledge Proficiency in Python, Java, or C++ Experience with data pipeline/workflow tools (e.g., Apache Airflow, Kubeflow) Strong problem‑solving skills and fast pace Excellent communication and collaboration Proven experience in AI/ML infra engineering or related roles on a hyperscaler platform for deploying large scale solutions
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Candidatura e Ritorno (in fondo)
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