1 to 25 of 97 MLOps Jobs

programming languages (such as Java) Experience of working on a project using agile concepts (such as working in sprints) Familiarity with working in an MLOps environment. Experience working with search engines (such as Elasticsearch more »

datasets, experience with Azure Data Factory, Azure SQL Database, and Cosmos DB. Understanding of CI/CD pipelines, containerization (Docker, Kubernetes), and experience implementing MLOps practices using Azure DevOps. Strong analytical and problem-solving abilities, with the capability to work on complex issues and drive innovative solutions. Deep understanding of more »

Physics, Machine Learning ● Deep expertise in Python (production-level) and SQL ● Proficiency in machine learning libraries (eg. Pandas, scikit-learn, TensorFlow) and experience with MLOps frameworks for model deployment ● Strong visualisation skills including experience with Tableau ● Familiarity with Git-based source control methodologies, including branching and pull requests ● Exceptional communication more »

Physics, Machine Learning ● Deep expertise in Python (production-level) and SQL ● Proficiency in machine learning libraries (eg. Pandas, scikit-learn, TensorFlow) and experience with MLOps frameworks for model deployment ● Strong visualisation skills including experience with Tableau ● Familiarity with Git-based source control methodologies, including branching and pull requests ● Exceptional communication more »

Physics, Machine Learning ● Deep expertise in Python (production-level) and SQL ● Proficiency in machine learning libraries (eg. Pandas, scikit-learn, TensorFlow) and experience with MLOps frameworks for model deployment ● Strong visualisation skills including experience with Tableau ● Familiarity with Git-based source control methodologies, including branching and pull requests ● Exceptional communication more »

learning: supervised & unsupervised · Instruct external annotators and manage annotation tasks · Written and verbal communication skills · Presentation skills Desirable skills · SQL · Cloud: AWS · Programming languages · MLOps processes and tools · Interest in the dairy industry is useful · Team leadership or mentoring experience What we’ll offer you in return Full-time, Permanent more »

healthcare data and extract meaningful information. Collaborate with cross-functional teams to integrate machine learning solutions into their products and services. Design and implement MLOps processes to ensure scalability, reliability, and performance of machine learning systems. Optimise model performance and accuracy through continuous experimentation and iteration. Stay updated on the more »

be an optional structured training programme to progress you onto management, a pathway to become an Architect, and even venture into the world of MLOps/AIOps. Key experience desired/what you will learn: Design, develop, and maintain scalable data pipelines and ETL processes leveraging GCP, AWS, and/ more »

cloud SDKs and APIs. Deep understanding of AI/ML to enhance pipeline efficiency, with experience in TensorFlow, PyTorch, AutoML, Python/R, and MLOps platforms like MLflow and Kubeflow. Solid background in DevOps , including CI/CD automation with Bitbucket Pipelines, Azure DevOps, and GitHub. Capability to automate the more »

be an optional structured training programme to progress you onto management, a pathway to become an Architect, and even venture into the world of MLOps/AIOps. Key experience desired/what you will learn: Design, develop, and maintain scalable data pipelines and ETL processes leveraging GCP services such as more »

be an optional structured training programme to progress you onto management, a pathway to become an Architect, and even venture into the world of MLOps/AIOps. Key experience desired/what you will learn: Design, develop, and maintain scalable data pipelines and ETL processes leveraging GCP services such as more »

and libraries. Experience with ML experiment tracking and collaboration tools like MLflow and Weights & Biases . A strong foundation in software engineering, DevOps practices, MLOps deployment, and infrastructure management. An affinity for generative AI frameworks and SDKs , such as RAG, Langchain, Semantic Kernel, and tools like MS tooling, Co-Pilot more »

handling unstructured data sets Familiarity with software engineering principles and collaborative programming best practices, including version control, testing, and deployment Knowledgeable in DevOps/MLOps methodologies and practices, with experience in cloud environments like Google Cloud Platform or Microsoft Azure Deep passion for data and analytics Proven track record in more »

datasets, experience with Azure Data Factory, Azure SQL Database, and Cosmos DB. Understanding of CI/CD pipelines, containerization (Docker, Kubernetes), and experience implementing MLOps practices using Azure DevOps. Strong analytical and problem-solving abilities, with the capability to work on complex issues and drive innovative solutions. Deep understanding of more »

frameworks (Dask, Modin) Hands on experience with Natural Language Processing (NLP) and Large Language Models (LLMs) Experience of creating and deploying microservices Knowledge of MLOps concepts (CI/CD, versioning, reproducibility, observability) and development best practices ABOUT US J.P. Morgan is a global leader in financial services, providing strategic advice more »

environments, ensuring seamless integration with legacy systems. S trong AI and ML principles , with a track record of model development, deployment, and mastery of MLOps practices. Experienced in crafting enterprise-scale cloud-native AI/ML solutions Proficiency in data pipeline orchestration across hybrid environments, leveraging the latest in Azure more »

be an optional structured training programme to progress you onto management, a pathway to become an Architect, and even venture into the world of MLOps/AIOps. Key experience desired/what you will learn: Design, develop, and maintain scalable data pipelines and ETL processes leveraging GCP services such as more »

techniques as well as evaluating metrics to drive optimizations using tools like Elastic stack, Prometheus, Alibi detect, etc. A solid background in DevOps and MLOps practices, and familiarity with tools to manage infrastructure as code, like Terraform and package managers like Helm Charts. Proficiency with pipeline orchestration tools, such as more »

Scientist in the Financial Services with experience in..... Statistical Models, Computer Vision, Predictive Analytics, Data Visualization, Large Language Models (LLM), NLP, AI, Machine Learning, MLOPs Python, Pyspark, Azure, Agile, MetaBase, then please apply. You can 📧 your cv to matt@hawksworthuk.com or message me on LinkedIn. Ideally you'll have plenty more »

and Large Language Models in practice. Proficiency in ML frameworks (e.g., PyTorch, TensorFlow, or Hugging Face Transformers). Familiarity with cloud-based ML services. MLOps experience, especially with versioning tools for data and models. Benefits: Competitive salary, negotiable on experience Options for equity in a well-positioned startup Strong and more »

with non-technical colleagues. Strong knowledge of Python and ML libraries (PyTorch, Tensorflow, Keras, etc.) with R/SQL knowledge, partnered with experience with MLOps tools (Kubernetes, Docker, Jenkins, etc.) would be advantageous. Strong knowledge and experience in deploying models into business processes, preferably in the healthcare, insurance or financial more »

Build and maintain efficient, scalable, and reliable AI infrastructure, tools, and pipelines to support the deployment of machine learning models in collaboration with the MLOps team. Continuously research and stay current with the latest advancements in AI, machine learning, and data science. Contribute to the growth of the AI team more »

would be an advantage. Other languages such as C# would be beneficial but not essential. · Machine Learning and Data Science Tools - Any experience in MLOps and productionising data science initiatives would be beneficial. Data Analysis and Storytelling - Proficiency in interpreting data analysis results and drawing meaningful conclusions Please don't more »

knowledge of machine learning algorithms applied to solving business problems. Proficiency in Python and popular Data Science frameworks (e.g. pytorch, pandas, numpy etc) and MLOps platforms (e.g. Sagemaker) Solid understanding of metrics, benchmarking and evaluation methodology AI-powered user-facing products. Experience working in highly collaborative environments with complex technical more »

tools and practices. Competency in deploying and managing AI solutions on AWS cloud infrastructure. Experience with containerization technologies like Docker. Understanding and application of MLOps practices for efficient model lifecycle management. Knowledge of data security and privacy practices. Effective problem-solving skills to address challenges encountered in AI development projects. more »