126 to 150 of 346 MLflow Jobs in the UK

Libraries: LangChain, Hugging Face Transformers, PyTorch, TensorFlow, Scikit-learn Agentic AI Tools: OpenAI GPT models, Crew,AI, Cohere, Pinecone (for vector databases), AutoGPT Data Engineering & ML Pipelines: Apache Airflow, MLflow, Kubeflow, dbt, Prefect Cloud & Deployment Platforms: AWS SageMaker, Azure ML, Google Vertex AI APIs & Orchestration: OpenAI API, Anthropic Claude, Weaviate, FastAPI (for AI applications) MLOps & Experimentation: Weights & Biases, DVC (Data More ❯

open source capabilities on public and private cloud infrastructure, Linux and Kubernetes. Our team applies expert insights to real-world customer problems, enabling the enterprise adoption of Ubuntu, Kubeflow, MLFlow, Feast, DVC and related analytics, machine learning and data technologies. We are working to create the world's best open source data platform, covering traditional SQL databases and today's More ❯

open source capabilities on public and private cloud infrastructure, Linux and Kubernetes. Our team applies expert insights to real-world customer problems, enabling the enterprise adoption of Ubuntu, Kubeflow, MLFlow, Feast, DVC and related analytics, machine learning and data technologies. We are working to create the world's best open source data platform, covering traditional SQL databases and today's More ❯

ll do: Build and deploy scalable ML solutions: Design, train, and deploy machine learning models and workflows with a focus on production-readiness, leveraging tools like Docker Containers, Kubernetes, MLflow, Kafka, and AWS Services. Leverage vector databases and streaming systems: Design and implement solutions with vector databases and Kafka to handle large-scale, high-dimensional, real-time data processing for … ML and AI pipelines. Standardize workflows: Use MLflow to manage the end-to-end ML lifecycle, including experiment tracking, model registry, and deployment. Automate and orchestrate: Use orchestration tools like Airflow to manage complex ML workflows and ensure seamless execution at scale. Optimize infrastructure: Design efficient ML pipelines and leverage cloud services like AWS to ensure reliable, scalable, and cost More ❯

and semantic similarity. Strong proficiency in Python, including use of ML libraries such as TensorFlow, PyTorch, or similar. Experience with data science tools and platforms (e.g., Jupyter, Pandas, NumPy, MLFlow). Familiarity with cloud-based AI tools and infrastructure, especially within the AWS ecosystem (SageMaker, Lambda, etc.). Strong understanding of data structures, algorithms, and statistical analysis. Experience working with More ❯

attention to detail, excellent communication skills, and a team-oriented mindset. Preferred qualifications, capabilities, and skills Master's degree in computer science, ML, or related areas. Experience with Ray, MLFlow, and/or other distributed training frameworks. Understanding of Search/Ranking, Recommender systems, Graph techniques, and other advanced methodologies. Deep knowledge of LLM techniques, including Agents, Planning, Reasoning. Experience More ❯

understanding of the marketing ecosystem, including media measurement solutions like media mix modelling. Experience with RNNs, NLP, Computer Vision, GenAI, CausalAI, GraphAI, and advanced techniques. Familiarity with versioning models (MLFlow), API design (FastAPI), and building custom dashboards (Dash). Why You’ll Love It Here: Variety and Challenge: No two projects are the same. You’ll work across multiple industries More ❯

and model performance Cloud and MLOps for Optimization Models: Familiarity with deploying and managing optimization models on cloud platforms (AWS, GCP, Azure) and employing MLOps practices (with tools like MLFlow, BentoML) to ensure efficient lifecycle management of optimization solutions Ethical AI and Continuous Learning: A robust understanding of AI ethics and privacy considerations, especially relevant to optimization, coupled with a More ❯

of data governance, compliance, and security best practices . Prior experience with cloud-based lakehouse implementation . Experience with machine learning (ML) and AI frameworks, particularly within Databricks (e.g., MLflow, AutoML, or PyTorch/TensorFlow). Understanding of AI-driven analytics and predictive modeling to enhance BI solutions. Think you can make a difference? WE ARE your future. More details More ❯

of data governance, compliance, and security best practices . Prior experience with cloud-based lakehouse implementation . Experience with machine learning (ML) and AI frameworks, particularly within Databricks (e.g., MLflow, AutoML, or PyTorch/TensorFlow). Understanding of AI-driven analytics and predictive modeling to enhance BI solutions. Think you can make a difference? WE ARE your future. Full time More ❯

databases such as SQL/NoSQL Experience with at least one Cloud Provider (AWS, Azure or GCP) Strong experience deploying and monitoring machine learning (MLOps), using tools such as MLflow, AWS Sagemaker, and Azure Machine Learning Experience in relevant Data Manipulation, Machine Learning and Statistical Analysis coding packages (eg. in Python: NumPy, Scikit-Learn, Pandas, Matplotlib etc.) Strong skills in More ❯

globally distributed group tackling novel R & D problems that directly impact customers. OUR TECHNOLOGY STACK: Python • Agentic tools (Autogen, Semantic Kernel, LangChain) • SQL • MongoDB • Third-party LLM APIs • LiteLLM • MLflow • Google Cloud Platform • Docker • PyTorch • Hugging Face Transformers • SPARQL • Kubernetes WHAT YOU WILL BE DOING AT BEAMERY Join us at the forefront of transforming how organisations manage talent and plan More ❯

augmented generation systems, Familiarity with MLOps, including Docker, CI/CD, or model deployment in cloud environments, Exposure to cloud platforms (Azure, GCP, AWS) and data tools such as MLflow, Airflow, Databricks, Previous consulting or client-facing experience, Contributions to open-source or technical publications a plus. Being You Diversity is a whole lot more than what we look like More ❯

MLOps Knowledge: Experience taking models from experiments through to production deployments, with tools such as Docker, Kubernetes & serverless alternatives such as AWS Lambda. Familiarity with MLOps tools such as MLFlow, Kubeflow or Sagemaker. A strong knowledge of cloud platforms (ideally AWS) and their respective services for deploying robust, AI-heavy applications. Bonus Experience: Experience with named entity recognition/recommendation More ❯

dashboards Deploying data science solutions on a cloud platform; Azure ML and MSFT Certificate are highly desirable MLOps is highly desirable, e.g. CI/CD, Feature Store, drift monitoring, MLflow, DVC, Docker, Kubernetes Software development experience is desirable Algorithm design experience is desirable Data architecture knowledge is desirable API design and deployment experience is desirable Big data (e.g. Spark) experience More ❯

augmented generation systems, Familiarity with MLOps, including Docker, CI/CD, or model deployment in cloud environments, Exposure to cloud platforms (Azure, GCP, AWS) and data tools such as MLflow, Airflow, Databricks, Previous consulting or client-facing experience, Contributions to open-source or technical publications a plus. Being You Diversity is a whole lot more than what we look like More ❯

dashboards Deploying data science solutions on a cloud platform; Azure ML and MSFT Certificate are highly desirable MLOps is highly desirable, e.g. CI/CD, Feature Store, drift monitoring, MLflow, DVC, Docker, Kubernetes Software development experience is desirable Algorithm design experience is desirable Data architecture knowledge is desirable API design and deployment experience is desirable Big data (e.g. Spark) experience More ❯

as TensorFlow, PyTorch, Scikit-learn, and Keras. Understanding of algorithms and techniques for supervised and unsupervised learning. Experience with tools for model monitoring, logging, and performance evaluation, such as MLflow or Prometheus. Strong scripting skills in Bash, PowerShell, or similar scripting languages for automation of tasks and ability to write reusable and maintainable code to streamline ML operations Proficient in More ❯

in Infrastructure-as-Code practices using AWS CDK, CloudFormation, or Terraform in production environments. Proven track record designing and operationalised end-to-end MLOps pipelines with tools such as MLflow, SageMaker Pipelines, or equivalent frameworks. Extensive experience building and operating containerised applications using Docker and Kubernetes, including production-grade orchestration and monitoring. Deep experience with CI/CD best practices More ❯

in Infrastructure-as-Code practices using AWS CDK, CloudFormation, or Terraform in production environments. Proven track record designing and operationalised end-to-end MLOps pipelines with tools such as MLflow, SageMaker Pipelines, or equivalent frameworks. Extensive experience building and operating containerised applications using Docker and Kubernetes, including production-grade orchestration and monitoring. Deep experience with CI/CD best practices More ❯

in Infrastructure-as-Code practices using AWS CDK, CloudFormation, or Terraform in production environments. Proven track record designing and operationalised end-to-end MLOps pipelines with tools such as MLflow, SageMaker Pipelines, or equivalent frameworks. Extensive experience building and operating containerised applications using Docker and Kubernetes, including production-grade orchestration and monitoring. Deep experience with CI/CD best practices More ❯

in Infrastructure-as-Code practices using AWS CDK, CloudFormation, or Terraform in production environments. Proven track record designing and operationalised end-to-end MLOps pipelines with tools such as MLflow, SageMaker Pipelines, or equivalent frameworks. Extensive experience building and operating containerised applications using Docker and Kubernetes, including production-grade orchestration and monitoring. Deep experience with CI/CD best practices More ❯

in Infrastructure-as-Code practices using AWS CDK, CloudFormation, or Terraform in production environments. Proven track record designing and operationalised end-to-end MLOps pipelines with tools such as MLflow, SageMaker Pipelines, or equivalent frameworks. Extensive experience building and operating containerised applications using Docker and Kubernetes, including production-grade orchestration and monitoring. Deep experience with CI/CD best practices More ❯

in Infrastructure-as-Code practices using AWS CDK, CloudFormation, or Terraform in production environments. Proven track record designing and operationalised end-to-end MLOps pipelines with tools such as MLflow, SageMaker Pipelines, or equivalent frameworks. Extensive experience building and operating containerised applications using Docker and Kubernetes, including production-grade orchestration and monitoring. Deep experience with CI/CD best practices More ❯

MLOps Knowledge Experience taking models from experiments through to production deployments, with tools such as Docker, Kubernetes & serverless alternatives such as AWS Lambda. Familiarity with MLOps tools such as MLFlow, Kubeflow or Sagemaker. A strong knowledge of cloud platforms (ideally AWS) and their respective services for deploying robust, AI-heavy applications. Bonus Experience Experience with named entity recognition/recommendation More ❯