MLOps Engineer

🏢 ОфисMiddleПолная занятостьПродуктовая

Есть о чём спросить

Навык востребован (Engineer). Но вилки нет, про деньги придётся договариваться с нуля.

Нажмите на сигнал, чтобы увидеть, на чём он основан

Чем предстоит заниматься

Automate training and retraining

Build ML data and feature pipelines

Build and evolve MLOps platform

Build model observability and monitoring

Capture production signals and expert feedback

Deploy models with safe rollout and rollback

Drive infrastructure reliability scalability improvements

Feed feedback into evaluation and retraining workflows

Implement human-in-the-loop systems

Maintain model registry and packaging

Operate batch streaming and real time model serving

Orchestrate data pipelines and workflows

Own experiment to production pipeline

Set up lakehouse data foundation with lineage and versioning

Support feature store capabilities

Стек

Active LearningApache AirflowApache IcebergBatch inferenceCI/CDContainersDagsterData LineageData QualityData labelingExperiment trackingFeature StoreHuman-in-the-loopImage dataInfrastructure as CodeKubernetesLakehouseMLflowMachine LearningModel ObservabilityModel RegistryMultimodal Machine LearningPythonReal TimeReal-time InferenceS3SQLSeries dataStreaming inferenceThe LoopTime SeriesTime Series DataWorkflow Orchestration“as-code”