1 to 25 of 42 MLflow Jobs in the UK excluding London

e.g. Spark, Databricks, Hadoop or cloud-native alternatives). Experience with containerisation and DevOps/MLOps tools (e.g. Docker, Kubernetes, Git, CI/CD, MLflow, Airflow). Strong experience with data engineering concepts (ETL/ELT, data integration, warehousing, vector databases/search) A little bit more about us... ...

Data modelling approaches (Kimball, Imnon) Orchestration tools - Apache Airflow, Prefect or cloud-native tools Backend software development (Java, APIs, Scalability, Logging and Monitoring etc.) MLFlow and other MLOps/Machine Learning Engineering processes to support advanced analytical use cases LLMs and Agentic AI BI tools such as Tableau/Power ...

/ML development experience. Experience deploying machine learning solutions into operational environments. Strong collaboration and stakeholder engagement skills. Desirable Skills Experience with MLOps, MLflow, Weights & Biases or similar experiment management platforms. Knowledge of cloud‐based AI platforms such as AWS, Azure or GCP and containerisation technologies including Docker. Experience with ...

/ML development experience. Experience deploying machine learning solutions into operational environments. Strong collaboration and stakeholder engagement skills. Desirable Skills Experience with MLOps, MLflow, Weights & Biases or similar experiment management platforms. Knowledge of cloud‐based AI platforms such as AWS, Azure or GCP and containerisation technologies including Docker. Experience with ...

Language Models (LLMs) into production applications. Model Ops (MLOps): Understanding the lifecycle of a model— from deployment and monitoring to versioning— using tools like MLflow or Kubeflow. Desired Experience & Context UK NatSec Landscape: Proven experience working within the UK National Security community (e.g., MoD, Home Office, or Intelligence Agencies). ...

Studio, OpenAI, AKS), GCP (Vertex AI, Cloud Run), AWS (Bedrock, SageMaker)* Experience building and automating AI/ML pipelines using tools such as MLflow, Kubeflow, Azure ML, Vertex Pipelines, Airflow or Google ADK* Hands-on experience with Generative and Agentic AI frameworks such as LangChain, LlamaIndex, CrewAI, Autogen, Google ...

statistical evaluation Proven track record transitioning models from experimentation to production with appropriate governance and quality controls Experience with experiment tracking and MLOps tooling (MLflow, Weights & Biases, Data Version Control) Experience developing LLM/GenAI applications, including prompt engineering and RAG architectures Familiarity with LLMOps tooling such as LangSmith, LangChain ...

international client communication, presentations), Expertise in at least one of the following domains: Experience with TensorFlow, PyTorch, XGBoost, LightGBM, Proficiency with Git, Linux, Docker, MLflow, Airflow, AWS/GCP/Azure, Solid SQL and data visualization skills. Additional Information Nice to have: Proven experience leading projects and managing teams, Experience ...

solutions in production cloud environments. Python: Strong capability in developing production-quality AI/ML code. LLMOps & AI Model Management: Familiarity with tools like MLflow, LangChain, Hugging Face, Kubeflow, or similar platforms. Data Processing: Working knowledge of Databricks/Spark or comparable large-scale data processing tools. SQL: Solid capabilities ...

worked with MLOps in practice rather than in principle: model versioning and registries, experiment tracking, automated retraining, and monitoring for drift and model degradation (MLflow, SageMaker, Databricks ML, Vertex AI or comparable) Are comfortable with ML frameworks such as scikit-learn, PySpark MLlib, or PyTorch/TensorFlow, enough to productionise ...

worked with MLOps in practice rather than in principle: model versioning and registries, experiment tracking, automated retraining, and monitoring for drift and model degradation (MLflow, SageMaker, Databricks ML, Vertex AI or comparable) Are comfortable with ML frameworks such as scikit-learn, PySpark MLlib, or PyTorch/TensorFlow, enough to productionise ...

worked with MLOps in practice rather than in principle: model versioning and registries, experiment tracking, automated retraining, and monitoring for drift and model degradation (MLflow, SageMaker, Databricks ML, Vertex AI or comparable) Are comfortable with ML frameworks such as scikit-learn, PySpark MLlib, or PyTorch/TensorFlow, enough to productionise ...

software development and implementation of AI/ML systems using cutting-edge technologies such as Python, R, Docker, Kubernetes, and tools like Azure Databricks, MLflow, and Google Vertex AI. Highly Valued Requirements Advanced Education: Master's or Ph.D. in big data, artificial intelligence, or related fields. Consulting Experience: Prior work ...

model risk reviews, security assessments, and secure deployment practices across client environments Stand up MLOps practices – experiment tracking, model versioning, evaluation, and monitoring (e.g. MLflow, Weights & Biases) Monitor deployed models and pipelines for drift, degradation, data issues, and cost, and iterate to improve them Containerise and deploy workloads with Docker … this role at present Desirable (Nice to Have) Experience with streaming and big‐data tooling – Apache Kafka, Spark, or Flink Experience with MLOps platforms – MLflow, Kubeflow, Vertex AI, SageMaker, or Weights & Biases Experience evaluating and optimising RAG systems at scale (retrieval quality, chunking, re‐ranking) Experience designing multi‐agent systems ...

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 ...

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 ...

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 ...

including Python, APIs, and distributed systems. Good understanding of MLOps tooling, including CI/CD, monitoring, and pipeline management. Experience with tools such as MLflow, Airflow, Evidently, DVC, Docker, or Kubernetes. Experience working within AWS, GCP, or Azure environments. Previous mentoring or leadership experience with an interest in developing management ...

data modelling, ETL and ELT processes, data quality, security and governanceAbility to collaborate effectively with technical and non-technical stakeholdersExperience with Unity Catalog, MLflow or Databricks Asset Bundles would be advantageousKnowledge of dbt, Kafka, Event Hubs or other data transformation and streaming technologies would be beneficialDesirable Experience: Databricks certificationsExperience working ...

Data Mesh concepts, data products, or federated data governance models. AI/ML Infrastructure: Exposure to AI/ML data foundations, including feature stores, MLflow, model serving, or retrieval pipelines (RAG). Regulated Sectors: Experience working within regulated environments (Financial Services, Healthcare, Public Sector). How You Work Pragmatic & Objective ...

Data Mesh concepts, data products, or federated data governance models. AI/ML Infrastructure: Exposure to AI/ML data foundations, including feature stores, MLflow, model serving, or retrieval pipelines (RAG). Regulated Sectors: Experience working within regulated environments (Financial Services, Healthcare, Public Sector). How You Work Pragmatic & Objective ...

methodologies for validation and processing of data against contracts. Enablement: Establish a stable data serving layer for Grafana and Analytics (via Databricks/MLflow), unblocking the Data Science team. Mentorship: Upskill the existing team in good practices (dbt, git workflows, SQL optimisation, data modelling). Experience & Background Pragmatic Architect: Capable ...

retraining Experience leading or mentoring engineering squads Hands‐on experience building and training deep learning models in PyTorch Familiarity with MLOps tooling such as MLflow and CI/CD workflows Experience with AWS, Azure, or GCP Experience with sequence models (e.g. transformers, RNNs) or generative models (e.g. diffusion, flow ...

RoCEv2, Spectrum-X, and fabric management. Solid expertise with Kubernetes, OpenShift, containerized workloads, GPU orchestration platforms, and AI/ML technologies such as Kubeflow, MLflow, JupyterHub, Run:AI, Rafay, ClearML, and MLOps solutions. Experience with HPC workload scheduling and resource management tools such as Slurm and PBS Pro, as well ...

design, develop and improve software for business and technology capabilities, focusing on scalable, secure ML deployments. You will lead MLOps with Python, SageMaker, Databricks, MLflow, kubernetes, and collaborate with data scientists, infra teams; ensure governance and risk management. The role emphasizes hands-on delivery and mentorship, contributing to a culture ...