ExpertsHub.ai · 4 hours ago
Senior Machine Learning Engineer
Charlotte, NC
Contract
Onsite
Senior LevelExpertsHub.ai is seeking a Senior Machine Learning Engineer to manage and optimize LLM models and MLOps pipelines. The role focuses on hands-on troubleshooting and production support for AI systems, requiring extensive experience with containerized services and AI inference systems.
Computer Software
Responsibilities
Managing, Operations and Support MLOps/LLMOps pipelines
Troubleshooting LLM models
Model optimization
Production support engineer who focusses on LLM models/AI and uses TensorRT LLM and Triton Inference Server
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
Qualification
Required
Managing, Operations and Support MLOps/LLMOps pipelines
Troubleshooting LLM models
Model optimization
Production support engineer who focuses on LLM models/AI and uses TensorRT LLM and Triton Inference Server
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 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
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
Company
ExpertsHub.ai
At ExpertsHub.ai, we bridge the gap between businesses and top-tier AI experts.
11-50 employees
Funding
Current Stage
Early StageCompany data provided by crunchbase