AI Operations Platform Consultant
Job Description:
- Brings extensive experience operating large-scale GPU-accelerated AI platforms, deploying and managing LLM inference systems on Kubernetes with strong expertise in Triton Inference Server and TensorRT-LLM.
- They have repeatedly built and optimized production-grade LLM pipelines with GPU-aware scheduling, load balancing, and real-time performance tuning across multi-node clusters. Their background includes designing containerized microservices, implementing robust deployment workflows, and maintaining operational reliability in mission-critical environments.
- They have led end-to-end LLMOps processes involving model versioning, engine builds, automated rollouts, and secure runtime controls.
- The candidate has also developed comprehensive observability for inference systems, using telemetry and custom dashboards to track GPU health, latency, throughput, and service availability.
- Their work consistently incorporates advanced optimization methods such as mixed precision, quantization, sharding, and batching to improve efficiency. Overall, they bring a strong blend of platform engineering, AI infrastructure, and hands-on operational experience running high-performance LLM systems in production
Basic Info:
- AI Operations Platform Consultant
- Experience deploying, managing, operating, and troubleshooting containerized services at scale on Kubernetes for mission-critical applications (OpenShift)
- Experience with deploying, configuring, and tuning LLMs using TensorRT-LLM and Triton Inference server.
- Managing MLOps/LLMOps pipelines, using TensorRT-LLM and Triton Inference server to deploy inference services in production
- Setup and operation of AI inference service monitoring for performance and availability.
- Experience deploying and troubleshooting LLM models on a containerized platform, monitoring, load balancing, etc.
- Operation and support of MLOps/LLMOps pipelines, using TensorRT-LLM and Triton Inference server to deploy inference services in production
- Experience deploying and troubleshooting LLM models on a containerized platform, monitoring, load balancing, etc.
- Experience with standard processes for operation of a mission critical system – incident management, change management, event management, etc.
- Managing scalable infrastructure for deploying and managing LLMs
- Deploying models in production environments, including containerization, microservices, and API design
- Triton Inference Server, including its architecture, configuration, and deployment.
- Model Optimization techniques using Triton with TRTLLM
- Model optimization techniques, including pruning, quantization, and knowledge distillation
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