4 of 4 Remote/Hybrid MLflow Jobs in Central London

learn, XGBoost, PyTorch, or TensorFlow Strong experience with cloud-based ML deployment (e.g., AWS SageMaker, Lambda, S3) Knowledge of MLOps/LLMOps tooling (e.g., MLflow, Weights & Biases, Data Version Control) Experience developing LLM/GenAI applications, including prompt engineering and RAG architectures Ability to absorb complex scientific and technical concepts ...

XGBoost, LightGBM, or CatBoost. Experience with model deployment tools (e.g., ONNX, TensorRT, TensorFlow Serving, TorchServe). Familiarity with ML lifecycle tools such as MLflow, Kubeflow, or Azure ML Pipelines. Experience working with distributed data processing (e.g., PySpark) and SQL. Understanding of software engineering best practices, including version control (Git). ...

training frameworks (DDP, FSDP, DeepSpeed). Background in NLP tasks such as NER, semantic similarity, QA, or dialogue systems. Experience with experiment tracking platforms (MLflow, Weights & Biases). Exposure to agentic AI paradigms (ReAct, chain-of-thought reasoning, tool use). Prior experience in leading AI research labs or high ...

turn ideas into impactful solutions. Optimize and deploy models with tools like TensorFlow Serving, TorchServe, ONNX, and TensorRT. Build and manage ML pipelines using MLflow, Kubeflow, and Azure ML Pipelines. Work with large-scale data using PySpark and integrate ML solutions into production environments. Monitor and improve model performance … LightGBM, CatBoost, TensorFlow, Keras, PyTorch). Familiarity with model deployment and serving tools (ONNX, TensorRT, TensorFlow Serving, TorchServe). Experience with ML lifecycle tools (MLflow, Kubeflow, Azure ML Pipelines). Knowledge of distributed data processing (PySpark) and software engineering principles (Git). A collaborative mindset and excellent problem-solving abilities. ...