26 to 50 of 121 MLflow Jobs in England

enhance your day-to-day work. Technologies and Tools Python Machine learning frameworks (e.g. PyTorch, TensorFlow, scikit-learn) ML/MLOps tooling (e.g. SageMaker, MLflow, TFServing) Spark and Databricks AWS services (e.g. IAM, S3, Redis, ECS) CI/CD tooling and best practices Streaming and batch data systems (e.g. Kafka ...

with rigorous validation of code, data transformations and analytical conclusions. Desirable Skills and Experience Experience with Azure Data Lake, Azure Data Factory, dbt, Airflow, MLflow, Power BI, Microsoft Purview or comparable tools. Experience with search indexes, vector databases, graph databases, knowledge graphs, ontologies or master-data-management techniques. Experience with ...

Face Transformers; OpenCV for vision applications. Strong understanding of experimental design, statistical evaluation, benchmarking and model validation. Experiment tracking and reproducibility tools, for example MLflow, Weights & Biases or equivalent. Data wrangling and analysis using Pandas, NumPy, SQL; familiarity with Spark or similar is a plus. Model optimisation and research ...

Fluent English – for international communication and collaboration. Additional Information Nice to have: Experience with LLMs, or Time Series, Familiarity with tools like Docker, MLflow, Airflow, AWS/GCP/Azure, Background in research or R&D, Experience working in cross-functional or international teams. Soft Skills: Curiosity and problem-solving ...

containerisation (Docker/Kubernetes). Strong background in data modelling, warehousing, and supporting analytical and ML workloads. Exposure to MLOps tools including MLFlow, kedro and Amazon Sagemaker. Development and maintenance of real time products like APIs using FastAPI/Flask. Operates autonomously with strong ownership and accountability. Strategic thinker ...

hands-on practice in Python.* Familiarity with popular machine learning libraries and frameworks such as scikit-learn, Hugging Face, PyTorch and TensorFlow.* Experience with MLFlow, AWS SageMaker and Bedrock is preferred.* Familiar with MLOps concept, SQL and PySpark* Experience with feature stores, machine learning models as service, and monitoring dashboards ...

Comfortable working with technical and non-technical stakeholders to deliver high-quality solutions. Even better if you have Databricks certifications. Experience with Unity Catalog, MLflow or Databricks Asset Bundles. Familiarity with dbt or similar data transformation frameworks. Exposure to Kafka, Event Hubs or other streaming technologies. Experience working with retail ...

where data comes from and how it will be used Even better ifyou have Experience with data versioning and experiment tracking tools e.g. DVC, MLflow, or Dagster asset lineage Experience preparing data for machine learning and longitudinal modelling, for example feature tables and train/test splits that avoid leakage ...

where data comes from and how it will be used Even better ifyou have Experience with data versioning and experiment tracking tools e.g. DVC, MLflow, or Dagster asset lineage Experience preparing data for machine learning and longitudinal modelling, for example feature tables and train/test splits that avoid leakage ...

/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 ...

Chroma, Weaviate, pgvector, Azure AI Search, or equivalent. Solid understanding of CI/CD pipelines for application code and ML/AI artifacts, including MLflow, DVC, or equivalent. Strong container and orchestration skills with Docker and Kubernetes, including the ability to deploy and troubleshoot AI inference workloads. Public cloud experience ...