1 to 25 of 38 Remote MLflow Jobs in London

data warehousing solutions (Snowflake, BigQuery, Redshift) Experience with cloud platforms (AWS, Azure, GCP) and their ML services (SageMaker, Azure ML, Vertex AI) Knowledge of MLOps tools including Docker, Kubernetes, MLflow, Kubeflow, or similar platforms Experience with version control (Git) and collaborative development practices Excellent analytical thinking and problem-solving abilities Strong communication skills with ability to explain technical concepts to More ❯

building end-to-end scalable ML infrastructure with on-premise DGX or cloud platforms including AWS EKS/SageMaker, Azure Machine Learning/AKS, or common ML platforms (ClearML, MLflow, Weights and Biases). Cloud & Automation: Strong understanding of AWS, Azure, containerization/Kubernetes, multiple automation/DevOps, and ML lifecycle practices. Data Handling: Practical knowledge in data wrangling, handling More ❯

Who We Are 🙋 For years, artists have been 'shooting in the dark' when it comes to their marketing. There's a clear lack of robust, transparent and accessible marketing tools for musicians and artists to use to promote their music. More ❯

Who We Are 🙋 For years, artists have been 'shooting in the dark' when it comes to their marketing. There's a clear lack of robust, transparent and accessible marketing tools for musicians and artists to use to promote their music. More ❯

Who We Are For years, artists have been 'shooting in the dark' when it comes to their marketing. There's a clear lack of robust, transparent and accessible marketing tools for musicians and artists to use to promote their music. More ❯

in DevOps, cloud infrastructure, or site reliability engineering Strong expertise in multi-cloud and hybrid infrastructure including AWS, GCP, and on-premises environments Experience with MLOps tooling such as MLFlow, Kubeflow, DataRobot, or similar platforms for ML lifecycle management Experience with containerization and orchestration (Docker, Kubernetes) specifically for ML workloads and GPU clusters Deep understanding of CI/CD pipelines More ❯

in DevOps, cloud infrastructure, or site reliability engineering Strong expertise in multi-cloud and hybrid infrastructure including AWS, GCP, and on-premises environments Experience with MLOps tooling such as MLFlow, Kubeflow, DataRobot, or similar platforms for ML lifecycle management Experience with containerization and orchestration (Docker, Kubernetes) specifically for ML workloads and GPU clusters Deep understanding of CI/CD pipelines More ❯

/B testing and statistical analysis to validate approaches Document ML systems and provide support for ongoing performance tuning Use tools like Python, TensorFlow, PyTorch, Scikit-learn, AWS, GCP, MLflow, Docker, SQL , and others Requirements Minimum Bachelors degree in Computer Science, Machine Learning, AI, or a related field Proven experience as a Machine Learning Engineer or in a similar role More ❯

prompt engineering techniques. Proficient in version control, CI/CD pipelines, and test-driven development. Nice to Have: Experience within financial services or fintech. Familiarity with MLOps tools (e.g., MLflow, Kubeflow, SageMaker). Understanding of regulatory or compliance frameworks. Academic background in mathematics, statistics, or quantitative disciplines. Who You Are: A natural problem-solver and independent thinker with a structured More ❯

prompt engineering techniques. Proficient in version control, CI/CD pipelines, and test-driven development. Nice to Have: Experience within financial services or fintech. Familiarity with MLOps tools (e.g., MLflow, Kubeflow, SageMaker). Understanding of regulatory or compliance frameworks. Academic background in mathematics, statistics, or quantitative disciplines. Who You Are: A natural problem-solver and independent thinker with a structured 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 ❯

Senior Data Engineer - (Azure/Databricks) page is loaded Senior Data Engineer - (Azure/Databricks) Apply locations London - Scalpel time type Full time posted on Posted 15 Days Ago job requisition id REQ05851 This is your opportunity to join AXIS More ❯

Lead Machine Learning Engineer - LLMs - Ramboll Tech At Ramboll Tech, we believe innovation thrives in diverse, supportive environments where everyone can contribute their best ideas. As a Lead Machine Learning Engineer, you will create cutting-edge AI solutions, mentor others More ❯

ingestion patterns, storage layers (Delta Lake), processing frameworks (Spark), and consumption mechanisms. Technology Selection: Evaluate and recommend optimal Databricks features and integrations (e.g., Unity Catalog, Photon, Delta Live Tables, MLflow) and complementary cloud services (e.g., Azure Data Factory, Azure Data Lake Storage, Power BI). Security & Governance Frameworks: Design robust data governance, security, and access control models within the Databricks More ❯

ingestion patterns, storage layers (Delta Lake), processing frameworks (Spark), and consumption mechanisms. Technology Selection: Evaluate and recommend optimal Databricks features and integrations (e.g., Unity Catalog, Photon, Delta Live Tables, MLflow) and complementary cloud services (e.g., Azure Data Factory, Azure Data Lake Storage, Power BI). Security & Governance Frameworks: Design robust data governance, security, and access control models within the Databricks More ❯

ingestion patterns, storage layers (Delta Lake), processing frameworks (Spark), and consumption mechanisms. Technology Selection: Evaluate and recommend optimal Databricks features and integrations (e.g., Unity Catalog, Photon, Delta Live Tables, MLflow) and complementary cloud services (e.g., Azure Data Factory, Azure Data Lake Storage, Power BI). Security & Governance Frameworks: Design robust data governance, security, and access control models within the Databricks More ❯

code (Docker, Terraform, etc) Build efficient data pipelines to handle text and audio data processing for ML models Deploy NLP models in cloud environments (AWS SageMaker) through Jenkins Use MLflow and other ML Ops applications to streamline ML workflows and adhere to data privacy and residency guidelines Communicate your work throughout the team and related departments Collaboration is a key More ❯

but not essential NLP/Deep learning experience (e.g. huggingface, spaCy) Deep learning framework experience (preferably PyTorch) MLOps experience (e.g. data and model versioning, model deployment CI/CD, MLFlow/DVC) Cloud platform experience, especially from an ML standpoint (AWS preferred) Statistical testing experience Experience with AWS Bedrock Experience with C# Containerization via Docker. Awareness of basic data science More ❯

Proven track record of leading complex AI projects in an agile environment Strong technical proficiency with modern ML tools and frameworks: PyTorch, TensorFlow, SparkMLLib, SciPy, Scikit-Learn, NLTK (etc) MLflow or similar ML lifecycle tools Cloud platforms (AWS/Azure/GCP) Experience with AI governance and responsible AI practices Understanding of public sector data requirements and compliance Outstanding communication More ❯

Proven track record of leading complex AI projects in an agile environment Strong technical proficiency with modern ML tools and frameworks: PyTorch, TensorFlow, SparkMLLib, SciPy, Scikit-Learn, NLTK (etc) MLflow or similar ML lifecycle tools Cloud platforms (AWS/Azure/GCP) Experience with AI governance and responsible AI practices Understanding of public sector data requirements and compliance Outstanding communication More ❯

to the delivery of complex business cloud solutions. The ideal candidate will have a strong background in Machine Learning engineering and an expert in operationalising models in the Databricks MLFlow environment (chosen MLOps Platform). Responsibilities: Collaborate with Data Scientists and operationalise the model with auditing enabled, ensure the run can be reproduced if needed. Implement Databricks best practices in More ❯

or graph models . Experience applying probabilistic models in real-world applications (e.g., recommendation systems, anomaly detection, personalized healthcare, etc.). Understanding of modern ML pipelines and MLOps (e.g., MLFlow, Weights & Biases). Experience with recent trends such as foundation models , causal inference , or RL with uncertainty . Track record of publishing or presenting work (e.g., NeurIPS, ICML, AISTATS, etc. More ❯

ability to translate complex analyses into actionable insights. Nice-to-Haves: Familiarity with marketing-specific measurement models such as Media Mix Modelling (MMM). Knowledge of model versioning (e.g. MLFlow), API frameworks (FastAPI), or building dashboards (e.g. Dash or Streamlit). The Opportunity: You’ll work across multiple industries and household-name brands, contributing to meaningful campaigns powered by cutting More ❯

development lifecycle with a strong focus on performance and maintainability. Collaborate cross-functionally with consulting and engineering teams to guide best practices. Drive innovation using tools such as Terraform, MLflow, AzureML, LangSmith, and more. Technical Requirements: Advanced proficiency in Python and modern software engineering practices. Experience architecting solutions using major cloud platforms (Azure, AWS, GCP). Familiarity with technologies such More ❯

and platform engineering teams to identify technical gaps, scope backend development efforts, and deliver core platform capabilities such as: Model training orchestration CI/CD for ML (e.g., using MLflow, Kubeflow, or Vertex AI Pipelines) Model versioning, monitoring, and governance Enable high-impact AdTech use cases including: Marketing Mix Modeling (MMM) Real-time personalization and bidding Audience segmentation and targeting More ❯