Our Client Currently looking for DataOps Engineer (AI Platform Engineer)

What you’ll actually do

• Close collaboration with infrastructure teams on selection and configuring GPU servers, high-performance networking, and RDMA-enabled clusters.
• Perform and manage GPU MIG configurations based on workload requirements and model characteristics.
• Ensure reliable and scalable GPU operations in Kubernetes, including runtime integration, device plugins, and GPU scheduling capabilities.
• Design, deploy, and maintain model serving runtimes, including vLLM, ONNX, SGLang, Nvidia Triton Runtimes, and KServe, ensuring high performance, scalability, and efficient GPU utilization.
• Build and maintain CI/CD pipelines and tooling for model packaging, versioning, and deployment, enabling reliable and model delivery for internal teams.
• Build and maintain platform tooling for model lifecycle management, including experiment tracking, model versioning, and registry systems (e.g. MLflow).
• Enable infrastructure and workflows for model fine-tuning and adaptation (e.g. LoRA), focusing on scalability, reproducibility, and automation within the platform.
• Develop and support internal tooling for managing model inputs and configurations (e.g. prompt templates), enabling consistent and reusable model usage patterns.
• Conduct performance testing and evaluation of multi-node GPU clusters to identify and resolve bottlenecks.
• Build and maintain observability for GPU clusters and model workloads, including metrics such as GPU utilization, memory usage, throughput, and latency.
• Integrate tracing for model inference workflows to provide end-to-end visibility into requests, and model behavior.
• Ensure compliance with security requirements for platform development.
• Evaluate and benchmark model inference performance across different runtimes, hardware setups, and configurations to guide platform optimization.

Who We’re Looking For

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field
• 5+ years of experience in infrastructure, platform engineering, or distributed systems, preferably in environments involving machine learning or GPU workloads
• Strong experience with Kubernetes, including deploying and operating production workloads
• Experience with Linux-based environments
• Strong programming skills in Python and/or Go
• Experience working with GPU infrastructure, including NVIDIA or AMD stack and multi-GPU environments will be considered highly advantageous
• Understanding of distributed systems and multi-node workloads
• Experience with model serving and inference systems (e.g. vLLM, ONNX, SGLang, Nvidia Triton Runtimes, KServe)
• Experience with CI/CD pipelines and automation for deploying services or models
• Experience with monitoring and observability tools (metrics, tracing, logging)
• Nice to have familiarity with networking concepts relevant to distributed systems (e.g. RDMA, high-performance networking)
• Good communication and problem-solving skills
• Ability to use advanced English for different work and business purposes
• Critical thinking and attention to detail
• Decision-making skills and the ability to adapt to new changes
• Ability to write concise and clear documentation
• Capability of dealing with constructive critics and knowing how to develop relationships with the team to achieve common goals

• Are you interested in this position?

   

  Apply by clicking on the “Apply Now” button below!

   

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