4 of 4 MLOps Jobs in the West Midlands

ready ML models. Prepare, transform, and validate complex datasets to ensure high-quality inputs for your models. Utilize cloud platforms (AWS, Azure, GCP) and MLOps practices for seamless deployment and monitoring of ML solutions. Write modular, production-grade code using Python and maintain version control with Git. What ...

your skills in a fast-moving AI environment. Nice to Have (But Not Essential) Experience with vector databases , embeddings, or prompt optimisation. Exposure to MLOps , model deployment, or monitoring tools. Background in data engineering, front-end development, or UX-aware AI applications . Interest or experience in healthcare or regulated ...

essential): Experience working with large language models via APIs or open-source frameworks. Fine-tuning or evaluating generative AI systems. Knowledge of MLOps, monitoring and lifecycle management. Experience with cloud AI/ML services and scalable data platforms. Exposure to reinforcement learning, graph machine learning or advanced deep learning techniques. ...

model reliability. Apply best practices in model evaluation, mitigating data leakage, and managing the bias/variance trade-off. Deploy and monitor models using MLOps approaches (Docker, CI/CD, cloud platforms like AWS/Azure/GCP). Collaborate closely with engineering and product teams to integrate ML solutions … model development, training, validation, and optimization. Proven experience deploying and maintaining production ML models. Hands-on experience with cloud platforms (AWS, Azure, GCP) and MLOps practices. Demonstrated ability to work with complex real-world datasets and perform feature engineering. Familiarity with modern ML frameworks and libraries, including PyTorch, TensorFlow, HuggingFace ...

model reliability. Apply best practices in model evaluation, mitigating data leakage, and managing the bias/variance trade-off. Deploy and monitor models using MLOps approaches (Docker, CI/CD, cloud platforms like AWS/Azure/GCP). Collaborate closely with engineering and product teams to integrate ML solutions … model development, training, validation, and optimization. Proven experience deploying and maintaining production ML models. Hands-on experience with cloud platforms (AWS, Azure, GCP) and MLOps practices. Demonstrated ability to work with complex real-world datasets and perform feature engineering. Familiarity with modern ML frameworks and libraries, including PyTorch, TensorFlow, HuggingFace ...