26 to 50 of 92 MLflow Jobs in London

/ML pipelines from data preparation through to production. Working with technical and business stakeholders to deliver AI solutions. Desirable experience includes MLflow, MCP, Docker, Kubernetes, GitLab CI/CD and Azure Data Factory. What’s on offer Up to £82,000 salary Annual performance bonus 28 days' holiday plus ...

modelling, ETL and ELT processes, data quality, security and governance Ability to collaborate effectively with technical and non-technical stakeholders Experience with Unity Catalog, MLflow or Databricks Asset Bundles would be advantageous Knowledge of dbt, Kafka, Event Hubs or other data transformation and streaming technologies would be beneficial Desirable Experience ...

technologies Preferred Qualifications, Capabilities, and Skills Background in STEM with exposure to machine learning systems Experience with MLOps tools and platforms (e.g., MLflow, Amazon SageMaker, Databricks, BentoML, Arize) Hands-on experience with cloud computing platforms like AWS, Azure, or GCP Experience working in highly regulated environments or industries #ICBCareers #ICBEngineering ...

software engineering skills in Python, with an understanding of how to write maintainable, testable code Familiarity with ML tooling and platforms such as Databricks, MLflow, Airflow, or equivalent Familiarity with CI/CD pipelines and how they apply to ML systems An understanding of ML lifecycle challenges - versioning, testing, monitoring ...

OpenAI, AI Foundry or AI Search, with Docker and Kubernetes Understanding of schema validation frameworks (e.g., JSON Schema, Pydantic) and MLOps tools such as MLflow or Airflow Strong communication ability to explain trade-offs around cost, latency and accuracy to technical and non-technical audiences Nice to have Experience using ...

refine problem statements and figure out how to solve them Some extra skills that are great (but not essential): Experience with MLOps tools: Airflow, MLflow, AWS SageMaker, AWS S3, AWS EMR, CI/CD Prior experience in the fraud domain and a strong understanding of fraud detection techniques Experience designing ...

solve real‐life problems and visualise the outcomes (e.g., seaborn) Proficient in working with cloud platforms (AWS preferred), code versioning (Git), experiment tracking (e.g., MLflow) Experience with cloud‐based ML tools (e.g., SageMaker), data and model versioning (e.g., DVC), CI/CD (e.g., GitHub Actions), workflow orchestration (e.g., Airflow/ ...

OpenCV. Production experience on AWS, specifically Amazon SageMaker: training and processing jobs, pipelines, model registry, endpoints. Practical MLOps: experiment tracking and model versioning with MLflow, hyperparameter optimisation, and reproducible, configuration‐driven training runs. Containerisation and infrastructure as code: Docker and CUDA‐based GPU images, plus enough Terraform to ship your ...

Experience with Spark, agile delivery methods and CI/CD practices Familiarity with DevOps and MLOps tools and practices, such as Docker, Terraform and MLFlow Confidence influencing and communicating with stakeholders across technical and non-technical teams Ideally, exposure to cloud infrastructure, NLP or large language models (such as fine ...

science stack (pandas, scikit‐learn, Jupyter, etc.)and version control tools like Git. Exposureto LLMOps, model monitoring, CI/CD and related technologies (Docker, MLflow,Kubernetes, FastAPI, etc.). Knowledgeand working experience of AGILE methodologies. At Ageas we offer a wide range of benefits to support you and your family ...

Apps, and medallion‐style data architectures. Experience with Azure AI Foundry, Azure OpenAI, LLM‐based application development, or agent SDKs and frameworks. Experience with MLflow, model lifecycle management, evaluation, tracing, and AI observability. Experience with containers, Docker, Kubernetes, AKS, OpenShift, or equivalent container platforms. Experience building reusable platform components, shared ...

Databricks. You'll work as a peer of the platform engineering teams. Rigour in running AI in production: experiment tracking and model registry (MLflow), evaluation frameworks and regression testing before release, monitoring and data-drift management, LLM observability and model governance. A track record of owning technical decisions across model ...

OpenCV. Production experience on AWS, specifically Amazon SageMaker: training and processing jobs, pipelines, model registry, endpoints. Practical MLOps: experiment tracking and model versioning with MLflow, hyperparameter optimisation, and reproducible, configuration-driven training runs. Containerisation and infrastructure as code: Docker and CUDA-based GPU images, plus enough Terraform to ship your ...

scikit-learn, NumPy, LightGBM, Pandas, LangChain/LangGraph, TensorFlow, etc.) PySpark AWS cloud infrastructure: EMR, ECS, Athena, etc MLOps/DevOps: Terraform, Docker, Airflow, MLFlow Monitoring and evaluation platforms including New Relic and Braintrust More information Enjoy fantastic perks like private healthcare & dental insurance, a generous work from abroad policy ...

stack (pandas, scikit‐learn, Jupyter, etc.) and version control tools like Git. Exposure to LLMOps, model monitoring, CI/CD and related technologies (Docker, MLflow, Kubernetes, FastAPI, etc.). Knowledge and working experience of AGILE methodologies. At Ageas we offer a wide range of benefits to support you and your ...

coding assistants in a responsible manner. Familiarity with DevOps practices and tools. Hands-on experience with experimentation and model tracking tools (e.g., MLFlow, Weights & Biases). Demonstrable impact of technical solutions or projects on decision-making. Experience working in fast-paced, agile environments. Customer-facing experience, notably in understanding ...

coding assistants in a responsible manner. Familiarity with DevOps practices and tools. Hands-on experience with experimentation and model tracking tools (e.g., MLFlow, Weights & Biases). Demonstrable impact of technical solutions or projects on decision-making. Experience working in fast-paced, agile environments. Customer-facing experience, notably in understanding ...

native software in production environments Engineering depth: Strength in backend (Python, Node.js, Java), frontend (React, Angular), APIs (REST/GraphQL) Machine Learning: Foundations of MLflow, Weights & Biases, LoRA, PEFT, SageMaker, Vertex AI, AzureML, Kubeflow, Airflow, Prefect, TorchServe, Triton, vLLM, SHAP, LIME, pandas, Spark, SQL LLM tooling: Familiarity with LangChain, Semantic ...

Capabilities, and Skills: Experience with agent frameworks (e.g., Google ADK, LangGraph) and agent protocols such as MCP, A2A, or AG-UI Experience with Databricks, MLflow, and Delta Lake or Apache Iceberg Exposure to model serving on Kubernetes and to model monitoring (drift, latency, accuracy) Familiarity with CRM platforms such ...

work streams Preferred Qualifications, Capabilities, and Skills Background in STEM with exposure to productionising machine learning systems Experience with MLOps tools and platforms (e.g., MLflow, Amazon SageMaker, Databricks, BentoML, Arize) Proficiency in cloud-native microservices architecture Hands-on experience with Amazon Web Services (AWS) Previous experience as a Platform engineer ...

their work without line-managing them Experience with a modern cloud data stack, ideally Snowflake (Snowpark, Cortex) and dbt Practical ML Ops experience, e.g. MLflow or similar, Git, CI/CD, containerisation, automated retraining and monitoring Experience with LLM and GenAI tools, both for your own development workflow ...

work independently, navigate ambiguity, resolve blockers, and deliver results with a strong sense of urgency It’s a bonus if you have Experience with MLFlow/model versioning, SageMaker Pipelines, or Databricks Familiarity with real-time, high-volume data products and low-latency data stores (e.g. DynamoDB) Automated testing ...

version control. Experience with AI assisted tooling Hands-on experience taking machine learning models into production and supporting them once live. Experience with MLflow or similar model lifecycle tooling, including deploying models for batch and/or near real-time use cases. Experience building or working with CI/ ...

working out what to build. Excellent communication skills, including explaining technical trade‐offs to non‐technical colleagues. Experience with Databricks (MLflow, Delta Lake, model serving). Solid ML fundamentals; exposure to PyTorch or classical ML is useful but not central to this role. Experience with multi‐modal models, particularly document ...

with data engineers on performance optimization, cost control, and platform capabilities MLOps & AI Platform (20%) Build ML model deployment pipelines using Azure ML, Databricks MLflow, or both Implement model versioning, experiment tracking, and model registry patterns Establish inference endpoints (batch and real‐time) with proper monitoring and governance Create reusable ...