application. High performance computing applications, libraries or parallel programming. ML frameworks e.g. PyTorch. DevOps tools e.g. CMake, Conan, Jenkins, Git/Gerrit, Nexus, Ansible, Terraform, Packer. Python programming. Operating system, compiler and debugger internals - for example the DWARF standard. Developing GDB, Perf, GCC or similar open source projects. GPU programming. More ❯
SQS, Redis Streams. Experience working with AWS services like EC2, ECR, RDS, S3, and IAM. Knowledge of Infrastructure-as-Code (IaC) and experience with Terraform for managing infrastructure deployment. Understanding of network infrastructure and cloud networking concepts, such as VPC, Subnet, Security Groups, and Load Balancing. Understanding Kibana/Datadog More ❯
code quality. Technologies/Tools we use: Python, Azure (Virtual Machines, Azure Web Apps, Cloud Storage, Azure ML), Anaconda packages, Git, GitHub, GitHub Actions, Terraform, SQL, Artifactory, Airflow, Docker, Kubernetes, Linux/Windows VMs. About You Hands-on industry experience in some combination of Software Engineering, ML Engineering, Data Science … engineering is necessary. Hands-on industry experience in some combination of the following technologies: Python ecosystem, Azure (VMs, Web Apps, Managed Databases), GitHub Actions, Terraform, Packer, Airflow, Docker, Kubernetes, Linux/Windows VM administration, Shell scripting (primary Bash but PowerShell as well). A solid understanding of modern security and More ❯
