51 to 75 of 113 MLflow Jobs in London

At Databricks, our core values are at the heart of everything we do; creating a culture of proactiveness and a customer-centric mindset guides us to create a unified platform that makes data science and analytics accessible to everyone. We More ❯

based with the right to work and able to be in the office 2–3 days per week Nice to Have Background in healthcare, clinical tech, or MLOps tooling (MLflow, Kubeflow, Vertex Pipelines) What’s on Offer 💸 Competitive salary + meaningful equity 🧘‍♂️ 25 days holiday + UK bank holidays 🌱 Flexible hours and a sustainable work pace 🌇 Bright, collaborative office minutes More ❯

based with the right to work and able to be in the office 2–3 days per week Nice to Have Background in healthcare, clinical tech, or MLOps tooling (MLflow, Kubeflow, Vertex Pipelines) What’s on Offer 💸 Competitive salary + meaningful equity 🧘‍♂️ 25 days holiday + UK bank holidays 🌱 Flexible hours and a sustainable work pace 🌇 Bright, collaborative office minutes More ❯

based with the right to work and able to be in the office 2–3 days per week Nice to Have Background in healthcare, clinical tech, or MLOps tooling (MLflow, Kubeflow, Vertex Pipelines) What’s on Offer Competitive salary + meaningful equity ‍♂️ 25 days holiday + UK bank holidays Flexible hours and a sustainable work pace Bright, collaborative office minutes 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 ❯

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 ❯

pressure. Nice-to-Have: Experience with marketing data or customer-level modelling (e.g., uplift, attribution, causal AI, graph AI, campaign optimization, spend optimization). Exposure to MLOps tools like MLflow, FastAPI, Airflow, or similar. Experience with experimentation and validation frameworks (e.g., A/B testing). Startup or freelance experience that required pace, clarity, and autonomy. Why This Role is More ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

interactive AI demos and proofs-of-concept with Streamlit, Gradio, or Next.js for stakeholder feedback; MLOps & Deployment: Implement CI/CD pipelines (e.g., GitLab Actions, Apache Airflow), experiment tracking (MLflow), and model monitoring for reliable production workflows; Cross-Functional Collaboration: Participate in code reviews, architectural discussions, and sprint planning to deliver features end-to-end. Requirements: Master’s degree in More ❯