We are hiring a Senior LLMOps / MLOps Engineer to support the deployment, operation, and ongoing reliability of machine learning systems in production. This role focuses on building and maintaining scalable model serving, ML-focused CI/CD workflows, and monitoring practices that help production models perform consistently over time. The position contributes at a senior level by improving the operational foundation for model delivery and lifecycle management.
• Model deployment & serving at scale — target L6 (Prof), must-have • CI/CD for ML models — target L6 (Prof), must-have • model monitoring & drift detection — target L6 (Prof), must-have • GPU infrastructure management — target L6 (Prof), nice-to-have • cost optimization for inference — target L6 (Prof), nice-to-have • versioning of models/datasets, — target L6 (Prof), nice-to-have • guardrail & safety pipeline implementation — target L6 (Prof), nice-to-have • Working AI leverage — observed and scored, never assumed • 4–10 years of relevant experience
Design, implement, and maintain scalable model deployment and serving workflows for production environments. Build and improve CI/CD processes for ML models to support reliable release and update practices. Establish monitoring and drift detection approaches to track model behavior and support operational response over time. Contribute to the broader ML operations environment through practices such as model and dataset versioning, and where applicable, support GPU infrastructure, inference cost optimization, and guardrail or safety pipeline implementation.
this position is for US based clients.
This role offers the opportunity to shape the production foundations behind machine learning systems and to improve how models are deployed, monitored, and maintained at scale. It is a strong fit for someone who wants to contribute senior-level operational expertise across the model lifecycle, with room to add value in areas such as infrastructure efficiency, versioning, and safety-oriented pipeline work.
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