Microsoft Recruitment 2024 For its Software Engineer, Microsoft Drive is organizing Off Campus Drive for candidates in Any Batch. In order to apply, candidates should have degree in Bachelors/Masters Degree. If you are interested, Please apply as soon as possible.
Company Name: Microsoft
Role: Software Engineer
Location: Work From Home
Experience: 1-2 Year
Qualification: Bachelors/Masters Degree
Batch: Any Batch
Job Type: Work From Home
Salary: Rs.3.4LPA
Microsoft Recruitment 2024
About Us :
Azure AI Infrastructure team is looking for passionate engineers to build the largest deep-learning infrastructure service at Microsoft. In this role you will be tasked with building new components to bring the latest innovations in AI Infrastructure onto the Azure AI Platform. You will partner with top engineering talent within Azure AI Infrastructure and across Azure to work on cluster orchestration, job scheduling, storage, networking, containerization and operating system integration. Your work will enable various AI languages and run-times on Azure AI Infrastructure to bring distributed deep learning training and inferencing to life. In addition, you will build infrastructure components required to build, deploy, monitor and service highly available and scalable Microsoft Service Fabric and Kubernetes clusters under your care. You will lead development and customer support from the frontline and establish architecture, service excellence guidelines and a high-quality bar.
Requirements:
- 1-3 years of experience with coding in one of C#, C or C++, Rust, go
- Experience working with the Linux operation system and Kubernetes cluster orchestration
- Experience with improving service operations or engineering fundamentals
- Excellent collaboration skills
- A master’s or bachelor’s degree in computer science or a related field
- At least 1 year of experience building and shipping production software or services
Responsibilities:
- Deliver a robust container orchestration platform for Azure AI Infrastructure
- Design and build the scheduling sub-system that is responsible for delivering on the SLAs for AI training and inferencing workloads
- Design and build storage and caching system for efficient DNN training and inferencing
- Design and build control plane APIs for creation and management of training jobs and inference model metadata
- Deliver node management, fault detection and node repair as a service to improve job/model reliability
- Deliver world-class monitoring systems and telemetry pipelines to enhance service and job observability for both end-users and operators.
- Codify security and compliance requirements by building and strengthening system defenses against malicious attacks and exploits
- Leverage performance and profiling tools to identify hot spots and bottlenecks across hardware and software boundaries: from CPU, GPU, microcode, OS, networking code and drive end-to-end job performance
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