Description:

Join the CloudWalk Wolfpack as a MLOps Engineer, focusing on the use of Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) to drive innovation.
Play a critical role in operationalizing the work of LLM Data Scientists, ensuring smooth deployment, efficient management, and scalable performance of LLMs across the infrastructure.
Deploy and manage LLMs using Kubernetes, Terraform, and cloud services to ensure adaptability to high-demand scenarios.
Optimize computing infrastructure by enhancing GPU utilization, distributed training, bandwidth efficiency between machines, and VPC connections.
Leverage cutting-edge technologies such as Hugging Face's Accelerate and PyTorch's torchrun for parallel training across multiple machines.
Collaborate with the R&D team to transition LLM and RAG technologies from conceptual stages to scalable, production-ready systems.
Monitor and improve system performance through advanced monitoring and logging practices, continuously seeking improvements.
Stay updated on industry advances in MLOps, cloud computing, and AI technologies to implement innovative solutions.

Requirements:

Solid experience with DevOps, cloud infrastructure, and deploying machine learning models is required.
Expertise in network optimization and parallel computing is crucial for this role.
A problem-solving mindset is necessary to navigate complex challenges and improve system efficiency.
Strong communication skills and the ability to work effectively within a dynamic, interdisciplinary team are expected.
A commitment to continuous learning and staying abreast of the latest technological advancements is essential.

By joining CloudWalk, you become part of a team that is reshaping the future with technological innovations.
The company values creativity, teamwork, and a dedication to excellence.
Your work will contribute to a mission of driving forward technological advancements.
CloudWalk encourages innovation and impact, inviting you to dare to join the Wolfpack.