We’re looking for a Senior Cloud Engineer to join our growing global IT team and help drive meaningful transformation across our business. You’ll play a hands-on role in replacing legacy systems, delivering scalable cloud-first solutions, and exploring the value AI can bring to our people and customers. This is an opportunity to work on real-world problems that impact how we operate, sell, and serve.
Design and implement scalable, secure cloud solutions in close collaboration with engineering and architecture teams, performing cloud engineering tasks within agile sprints using established best practices and templates. This role involves building new cloud environments for customers using native AWS or Azure services, utilizing fundamental architectural concepts to build automated infrastructure, CI/CD processes, and supporting toolsets.
Join a growing team of experts working on Veeam's SaaS platform as a Site Reliability Engineer. You will design, implement, and maintain scalable and reliable infrastructure solutions on Microsoft Azure. Automate deployments, maintain a resilient SaaS application platform, and continuously improve system reliability, performance, and scalability. Participate in on-call rotations and meet standards for information security and compliance.
As a DevOps Engineer, you will be working on improving the Perfectscale by DoiT product infrastructure. You'll be collaborating with product managers, and developers from other teams and also participate in product-making decisions. You will own Cloud and Kubernetes Environments, Design, operate, and maintain highly available production(and non-production) K8s clusters across multiple cloud environments.
Looking for a Senior DevOps Engineer to help support our hybrid cloud infrastructure across GCP and AWS. Designing, implementing, and maintaining scalable, automated CI/CD pipelines using GitOps practices tailored for GCP and AWS environments. Managing and optimizing Kubernetes clusters to ensure high availability, security, and scalability of containerized applications.
As the Director of Compute Platform, you’ll lead teams responsible for Yelp’s compute platform, driving initiatives in compute infrastructure and cloud economics. This director-level role shapes technical strategy, empowers team members, and partners with stakeholders across engineering and business disciplines. Your work will directly impact the efficiency, reliability, and scale of Yelp’s systems, with broad influence across the company.
As a Site Reliability Engineer at Masabi, you will ensure the platform's reliability, performance, and security. In this role, you'll be pivotal to scaling and modernising our platform while ensuring uptime, performance, and security. You'll work across legacy and modern infrastructure, drive key improvements, and collaborate closely with architecture and product teams to enable reliable delivery across the business.
The Platform Engineering team at MOO is focused on building and operating the infrastructure, tooling, and systems that empower our engineering teams to deliver high-quality software quickly and safely. We’re hiring a Platform Engineering Manager to lead our team as we transition from Kubernetes to a modern Serverless architecture on AWS. This role is key to evolving our internal platform, improving the developer experience, and supporting the wider business strategy.
As a Contract Engineer, you’ll join a fast-moving delivery team shaping the future of secure infrastructure. You’ll get hands-on with Wolfi and Chainguard Images, working with an all-star cast of engineers and security experts who want to build (and break things) right alongside you. This is a 3 month contract role, with the potential to extend to 6 months.
Make a huge impact at Dailymotion by joining our team as an MLOps Engineer. You will play a crucial role in bridging the gap between machine learning and engineering. Collaborating with the teams to deliver innovative and high-quality data products. Empowering ML Engineers by providing tools and infrastructure that enhance their productivity while adapting to their specific needs. Accelerating time-to-market for the first production-ready versions of ML products by ensuring seamless integration, proper service connections, and access to data and resources. Ensuring ML Engineers maintain control over their models in production, allowing them to monitor, troubleshoot, iterate quickly, and refine models and feature engineering logic.