Machine Learning Operations Lead
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Die Stelle
Lead the design, delivery, and operation of production ML inference and fine-tuning services across serverless and multi-cluster deployments.
Own availability and performance SLAs, drive incident response, and conduct postmortems to prevent recurrence.
Build and scale testing, deployment, configuration management, and monitoring practices in collaboration with Infra SREs.
Define and enforce configuration best practices for inference engines (vLLM, tvLLM, Pulsar) to prevent runtime issues.
Develop self-serve tooling and internal developer platforms to reduce operational toil for ML engineers and customers.
Lead, mentor, and grow an MLOps team while partnering with infrastructure and ML engineering to improve reliability and cost efficiency.
Own availability and performance SLAs, drive incident response, and conduct postmortems to prevent recurrence.
Build and scale testing, deployment, configuration management, and monitoring practices in collaboration with Infra SREs.
Define and enforce configuration best practices for inference engines (vLLM, tvLLM, Pulsar) to prevent runtime issues.
Develop self-serve tooling and internal developer platforms to reduce operational toil for ML engineers and customers.
Lead, mentor, and grow an MLOps team while partnering with infrastructure and ML engineering to improve reliability and cost efficiency.
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