226 to 250 of 323 MLflow Jobs in England

Galytix (GX) is delivering on the promise of AI. GX has built specialised knowledge AI assistants for the banking and insurance industry. Our assistants are fed by sector-specific data and knowledge and easily adaptable through ontology layers to reflect More ❯

As a Big Data Solutions Architect (Resident Solutions Architect) in our Professional Services team, you will work with clients on short to medium-term customer engagements on their big data challenges using the Databricks platform. You will provide data engineering More ❯

environments and guiding organisations through early-stage discovery. Desirable: Certifications in Data Architecture, Governance, or Cloud. Experience working with or implementing DDOM frameworks. Understanding of ML Ops tools (e.g., MLflow, SageMaker, Kubeflow) and data privacy regulations (GDPR, CCPA). 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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

PyTorch Knowledge of Natural Language Processing (NLP) and Computer Vision techniques Strong understanding of probability concepts and the machine learning lifecycle Experience with workflow and pipelining frameworks (e.g., Kubeflow, MLFlow, Argo) Awareness and application of Ethical AI principles This role offers a unique opportunity to work on cutting-edge projects, expand your expertise, and contribute to innovative AI and data 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 ❯

tools such as CloudFormation or Terraform Monitoring and performance tuning of cloud-based applications and services Nice to haves: (MLOps): Model Deployment & Serving – Deploy and manage ML models using MLflow, Azure ML, SageMaker, or similar, ensuring scalability and performance. Monitoring & Retraining – Set up model drift detection, performance monitoring, and automated retraining ML Pipelines & CI/CD – Automate end-to-end More ❯

enables you to take part in day-to-day conversations in the team and contribute to deep technical discussions Nice to Have Experience with operating machine learning models (e.g., MLFlow) Experience with Data Lakes, Lakehouses, and Warehouses (e.g., DeltaLake, Redshift) DevOps skills, including terraform and general CI/CD experience Previously worked in agile environments Experience with expert systems Perks More ❯

We expect Engineering Managers to be fluent in the programming language, architecture, and components that their team uses, in this case popular open-source machine learning tools like Kubeflow, MLFlow, and Feast. Code reviews and architectural leadership are part of the job. The commitment to healthy engineering practices, documentation, quality and performance optimisation is as important, as is the requirement More ❯

in model development, evaluation, and deployment. Proficiency with relevant ML libraries and frameworks such as PyTorch, TensorFlow, scikit-learn, HuggingFace or similar. Experience with modern ML tooling, such as MLflow, Jupyter, feature stores, and vector databases. Understanding of software engineering best practices including version control, testing, CI/CD, containerisation, and observability. Familiarity with MLOps principles - automated pipelines, model versioning More ❯

systems in production. Deep understanding of the ML lifecycle and associated engineering challenges (feature pipelines, model deployment, observability, drift detection, retraining). Proficient in Python and tools such as MLflow, Airflow, Docker, and cloud services (ideally Azure). Skilled in building scalable, maintainable ML pipelines using modern engineering practices. Experienced with Infrastructure as Code (IaC) and able to define, manage 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 Experience with AI governance and responsible AI practices Understanding of public sector data requirements and compliance Outstanding communication and stakeholder management skills Demonstrated ability to More ❯

We have a fantastic opportunity for an experienced Data Scientist to join our Commercial Analytics team. You’ll be building end to end data science solutions to solve some of AXA Commercial’s toughest problems and deliver significant business value.It More ❯

startup environments UK-based and available to work 2–3 days per week in-office (London) Bonus Points Experience in healthcare, medtech, or clinical systems Familiarity with MLOps tooling (MLflow, Kubeflow, Vertex Pipelines More ❯

startup environments UK-based and available to work 2–3 days per week in-office (London) Bonus Points Experience in healthcare, medtech, or clinical systems Familiarity with MLOps tooling (MLflow, Kubeflow, Vertex Pipelines More ❯

startup environments UK-based and available to work 2–3 days per week in-office (London) Bonus Points Experience in healthcare, medtech, or clinical systems Familiarity with MLOps tooling (MLflow, Kubeflow, Vertex Pipelines 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. Strong understanding of data structures, algorithms, and statistical analysis. Experience working with ETL pipelines and structured 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. Strong understanding of data structures, algorithms, and statistical analysis. Experience working with ETL pipelines and structured More ❯

FastAPI, or other common web frameworks. An understanding of core concepts in ML, data science and MLOps. Nice-to-Have : Built agentic workflows/LLM tool-use. Experience with MLFlow, WandB, LangFuse, or other MLOps tools. Experience with AirFlow, Spark, Kafka or similar. Why Plexe? Hard problems: we're automating the entire ML/AI lifecycle from data engineering to More ❯