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Mid-level ML role, metro location, and popular LLM/MLOps skills create high applicant density.
Requires specialized ML, LLM, and MLOps platform experience, limiting cross-industry transferability.
Explicit 4-8 years plus mandatory ML/LLM/MLOps and framework requirements imply high shortlisting strictness.
Develop and maintain Kubogent's machine learning model training and inference capabilities, including reusable training jobs and fine-tuning workflows.
Own end-to-end ML workflows: data preparation, training, evaluation, experiment tracking, model lifecycle management, and production-grade pipeline implementation.
Collaborate closely with platform engineers to integrate ML workflows into Kubogent's Kubernetes-based infrastructure and shape product features and APIs based on ML operations.
4-8 years of hands-on experience in machine learning engineering with substantial practice in training and fine-tuning models, including large language models.
Strong Python programming skills with experience in mainstream ML frameworks such as PyTorch or TensorFlow.
Experience with MLOps tooling like MLflow covering experiment tracking, model versioning, and reproducibility.
Experience working with real-world data: cleaning, transformation, dataset preparation, and validation.
Experienced in developing production-grade ML systems that integrate training, inference, and lifecycle management within Kubernetes environments.
Comfortable with advanced ML workflows encompassing distributed training, model evaluation pipelines, and performance optimization at scale.
Capable of translating experimental ML research into maintainable, repeatable platform-level capabilities for other engineering teams.