26 to 41 of 41 MLflow Jobs in England

in programming languages such as Python or R, with extensive experience with LLMs, ML algorithms, and models. Experience with cloud services like Azure ML Studio, Azure Functions, Azure Pipelines, MLflow, Azure Databricks, etc., is a plus. Experience working in Azure/Microsoft environments is considered a real plus. Proven understanding of data science methods for analyzing and making sense of More ❯

globally distributed group tackling innovative R&D challenges 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 Strategic AI Leadership Define and implement the company's AI and data science More ❯

required: AWS Data/ML Engineering & ML Ops (ECS, Sagemaker) CI/CD pipelines (GitLab, Jenkins) Python, PySpark & Big Data ecosystems AI/ML lifecycle, deployment & monitoring MLOps tooling (MLflow, Airflow, Docker, Kubernetes) Front-end exposure (HTML, Flask, Streamlit) RESTful APIs & backend integration If this ML Engineer role is of interest, please apply now for immediate consideration. More ❯

of cutting-edge techniques for Natural Language Processing and Computer Vision Strong grasp of basic probability concepts and machine learning lifecycle Experience with workflow and pipelining frameworks (e.g., Kubeflow, MLFlow, Argo) Understanding and application of Ethical AI considerations Ready to take your career to the next level? Apply today and be part of something extraordinary! Please either apply by clicking More ❯

A/B testing Experiment design and hypothesis testing MLOps & Engineering Scalable ML systems (batch and real-time) ML pipelines, CI/CD, monitoring, deployment Familiarity with tools like MLflow, Kubeflow, Airflow, Docker, Kubernetes Strategic skills Align ML initiatives with business goals Prioritize projects based on ROI, feasibility, and risk Understand market trends and competitive ML strategies Communicate ML impact More ❯

Experience Academic background (research Masters level) or industry experience in a relevant field Strong experience managing on premise Kubernetes clusters Deep knowledge of Kubeflow or similar systems such as MLflow Proficient in Python and experienced with Linux systems Familiar with AWS services such as Cognito, S3, EC2 and Lambda Experience working with ML frameworks such as PyTorch or Lightning Capable More ❯

Git) and testing. Collaborative spirit and a playful can-do attitude. Persistent with a well-rounded view on creating solutions. Experience with tools and platforms such as Databricks and MLflow for ML training and experimenting is highly desirable.Applications are reviewed on an ongoing basis. However, please note we do amend or withdraw our jobs and reserve the right to do More ❯

channels. What you’ll need: • Python, SQL, and data modelling expertise • Advanced knowledge of Snowflake & Snowpark • Confident communicator who can influence and collaborate • Experience building and managing ML environments (MLflow or similar) • Familiarity with CI/CD practices What you’ll do: • Architect and implement scalable ML environments with Data Science • Define best practices for deployment, monitoring, and governance • Build More ❯

channels. What you’ll need: • Python, SQL, and data modelling expertise • Advanced knowledge of Snowflake & Snowpark • Confident communicator who can influence and collaborate • Experience building and managing ML environments (MLflow or similar) • Familiarity with CI/CD practices What you’ll do: • Architect and implement scalable ML environments with Data Science • Define best practices for deployment, monitoring, and governance • Build More ❯

LangChain/LangGraph, LlamaIndex Experience with Hugging Face and LoRA/QLoRA for fine-tuning Experience with RAG & Vector DBs eg. FAISS, Weaviate, Pinecone Any experience of MLOps with MLFlow, AWS (SageMaker), CI/CD (GitHub Actions) or similar would be a benefit to an application The employer is well known not only for the forward-thinking approach they have More ❯

personalised experiences across global brands and channels. What you'll need Python, SQL, data modelling Expertise in Snowflake & Snowpark Immaculate communication to influence and collaborate Experience building ML environments (MLflow or similar) CI/CD practices What you'll do Design and implement a scalable ML environment with Data Science Define best practice for deployment, monitoring, and governance Build pipelines More ❯

modal models that combine vision and language Strong grasp of data-centric AI practices - annotation tooling, prompt evaluation, and dataset curation Familiarity with MLOps tools (e.g. Weights & Biases, SageMaker, MLflow) Experience working in regulated sectors like insurance, banking, or property What You'll Be Doing This is a hands-on, high-impact role - you'll be building production-grade AI More ❯

environment. Preferred Experience: Solid understanding of cyber security concepts such as threat detection, SIEM, anomaly detection, and incident response. Experience with tools for tracking ML models in production (e.g., MLflow). We encourage you to apply even if your experience is not a 100% match with the position. We are looking for someone with relevant skills and experience, not a More ❯

Your day-to-day will include: Building and maintaining end-to-end data pipelines and feature engineering workflows. Deploying and monitoring ML models in production using tools such as MLflow, Vertex AI, or Azure ML. Driving best practices in MLOps, including CI/CD, experiment tracking, and model governance. Supporting the data warehouse and ensuring data quality, governance, and accessibility. … experience in data or ML engineering. Strong knowledge of Python and SQL. Hands-on experience with cloud platforms (GCP or Azure) and Databricks. Familiarity with deploying ML workflows using MLflow, Vertex AI, or Azure ML. Nice-to-have: Experience with Spark, CI/CD pipelines, and orchestration tools. Knowledge of Elasticsearch or digital/web analytics platforms. Understanding of the More ❯

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. … such as: pandas, NumPy, scikit-learn XGBoost, LightGBM, CatBoost TensorFlow, Keras, PyTorch Experience with 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 … such as: pandas, NumPy, scikit-learn XGBoost, LightGBM, CatBoost TensorFlow, Keras, PyTorch Experience with 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 More ❯

Overview The Strategy & Operations organisation is the in-house dedicated team that supports the GTM and cross-functional teams within Databricks. This team embeds within the business as true partners, working directly with the "client team" on a mixture of More ❯