MLOps Engineer (LLM Serving and Infrastructure)

What You’ll Do:

• Deploy and Manage LLMs: Employ Kubernetes, Terraform, and cloud services to deploy and scale LLMs efficiently, ensuring their adaptability to high-demand scenarios.
• Optimize Computing Infrastructure: Focus on enhancing GPU utilization, distributed training, bandwidth efficiency between machines, and VPC connections to maximize system performance.
• Leverage Cutting-Edge Technologies: Utilize libraries such as Hugging Face's Accelerate and PyTorch's torchrun to facilitate parallel training across multiple machines in a cluster, optimizing our AI models' training and inference processes.
• Collaborate on Innovation: Partner with our R&D team to transition LLM and RAG technologies from conceptual stages to scalable, production-ready systems.
• Monitor and Improve System Performance: Implement advanced monitoring and logging practices to ensure system reliability and performance, continuously seeking improvements.
• Stay Updated on Industry Advances: Actively pursue the latest developments in MLOps, cloud computing, and AI technologies to implement innovative solutions and maintain our infrastructure's leading edge.

Technologies You Will Work With:

• Kubernetes, Terraform, and cloud computing platforms for scalable AI model deployment.
• CI/CD pipelines, Git for version control, and Bash scripting for operational efficiency.
• Hugging Face's Accelerate and PyTorch's torchrun for parallel training and optimization across multiple machines.
• A comprehensive understanding of network infrastructure to optimize bandwidth and secure VPC connections is essential.

What We Expect From You:

• Technical Mastery: Solid experience with DevOps, cloud infrastructure, and deploying machine learning models. Expertise in network optimization and parallel computing is crucial.
• Problem-Solving Mindset: The ability to navigate complex challenges, strategically manage resources, and improve system efficiency.
• Collaborative Approach: Strong communication skills and the ability to contribute effectively within a dynamic, interdisciplinary team.
• Lifelong Learner: A commitment to continuous learning, staying abreast of the latest technological advancements, and applying innovative solutions.