IT.HR Recruitment Agency · Albania, Andorra, Austria, Belarus, Belgium, Bosnia and Herzegovina, Bulgaria, Croatia, Cyprus, Czechia, Denmark, Estonia, Faroe Islands, Finland, France, Germany, Gibraltar, Greece, Guernsey, Holy See (Vatican City State), Hungary, Iceland, Ireland, Isle of Man, Italy, Jersey, Kosovo, Latvia, Liechtenstein, Lithuania, Luxembourg, Malta, Moldova, Monaco, Montenegro, Netherlands, North Macedonia, Norway, Poland, Portugal, Romania, San Marino, Serbia, Slovakia, Slovenia, Spain, Svalbard and Jan Ma
Skills: Python, AWS, Docker, CI/CD
Our Client is a long-established and highly-regarded Central and Eastern European technology provider specialising in designing, building, and operating advanced, mission-critical software solutions for large international organisations. With over 25 years of history and a team of 600+ top-tier software engineers across Europe, they are a certified partner for high-stakes industries including Aerospace & Defence, Automotive, Telecommunications, and Financial Services. They hold rigorous quality and security certifications such as NATO AQAP and TISAX.
This specific role involves working with a leading European client to develop and maintain their sophisticated cloud platform. You will be responsible for defining the DevOps architecture and strategy for solutions built primarily on Microsoft Azure and Kubernetes. You will be a key player in ensuring that highly available, scalable, and secure cloud-native architectures are implemented, managing the full lifecycle from Infrastructure as Code (IaC) development through to CI/CD and observability. This is a chance to apply your senior expertise to complex, enterprise-grade cloud environments.
Responsibilities --------------------
As a Senior DevOps Engineer specialising in AI and Data, you will be key to architecting and maintaining the infrastructure that supports large-scale AI/ML workloads. Your main responsibilities will be:
Platform Deployment and Management: Design, build, and maintain scalable and secure cloud infrastructure for AI and Big Data solutions, ensuring high availability and performance in a production environment. AI/ML Infrastructure Automation: Implement end-to-end automation for the complete Machine Learning (ML) lifecycle, including infrastructure for model training, real-time inference, and data processing. Infrastructure as Code (IaC): Develop, manage, and optimise Infrastructure as Code (IaC) using tools like Terraform to provision and configure cl …truncated… rking. Kubernetes for Production: Expert…
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