3 of 3 Exploratory Data Analysis Jobs in the UK

model deployment and serving tools: ONNX, TensorRT, TensorFlow Serving, TorchServe Familiarity with ML lifecycle tools: MLflow, Kubeflow, Azure ML Pipelines Experience working with distributed data processing using PySpark. Solid understanding of software engineering principles and version control (e.g., Git). Excellent problem-solving skills and ability to work independently … team. Collaborate with cross-functional teams to integrate AI solutions into scalable products Ensure best practices in data engineering and contribute to architectural decisions Support senior team members in identifying and addressing data science opportunities. Required Skills & Experience Proven experience in MLOps or DevOps roles within machine learning

impactful projects in a collaborative, innovation-driven environment. Key Responsibilities Design and implement robust machine learning models using modern frameworks and libraries. Collaborate with data scientists, engineers, and stakeholders to translate business requirements into technical solutions. Optimize and deploy models using 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 models into production environments. Monitor model performance and retrain as needed to ensure accuracy and efficiency. Collaborate with cross-functional teams to integrate AI solutions into scalable products

techniques including Q-learning , policy gradients , and RLlib Collaborate with cross-functional teams to integrate AI solutions into scalable products Ensure best practices in data engineering and contribute to architectural decisions Contribute to the mentoring and development of junior team members. Support senior team members in identifying and addressing … data science opportunities. Required Skills & Experience Proven experience with Transformer models and LLMs (GPT, LLaMA, Mistral, Claude) Strong background in fine-tuning and prompt engineering Hands-on experience with RAG pipelines Familiarity with Agent Frameworks (LangChain, LlamaIndex, CrewAI, AutoGen) Solid understanding of reinforcement learning concepts and tools (Q-learning