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