1 to 25 of 46 Remote MLflow Jobs in the UK

C# or Java, cloud SDKs and APIs. AI/ML expertise for pipeline efficiency, familiar with TensorFlow, PyTorch, AutoML, Python/R, and MLOps (MLflow, Kubeflow). Solid in DevOps, CI/CD automation with Bitbucket Pipelines, Azure DevOps, GitHub. Automate deployment of data pipelines and applications using Bash, PowerShell More ❯

Environment: Python, PyTorch or TensorFlow AWS (including SageMaker, S3, Lambda) or Azure ML Docker, Kubernetes, Airflow CI/CD tools (e.g. GitHub Actions, Jenkins) MLflow or similar frameworks Required Experience: Proven track record of deploying machine learning models to production Strong programming skills in Python and familiarity with modern ML More ❯

Environment: Python, PyTorch or TensorFlow AWS (including SageMaker, S3, Lambda) or Azure ML Docker, Kubernetes, Airflow CI/CD tools (e.g. GitHub Actions, Jenkins) MLflow or similar frameworks Required Experience: Proven track record of deploying machine learning models to production Strong programming skills in Python and familiarity with modern ML More ❯

security best practices . Prior experience with cloud-based lakehouse implementation . Experience with machine learning (ML) and AI frameworks, particularly within Databricks (e.g., MLflow, AutoML, or PyTorch/TensorFlow). Understanding of AI-driven analytics and predictive modeling to enhance BI solutions. Think you can make a difference? WE More ❯

Designing and fine‑tuning foundation‑model solutions; RAG patterns; prompt‑engineering & evaluation MLOps/LLMOps: CI/CD for models, feature stores, experiment tracking (MLflow, SageMaker, Azure ML, Vertex, etc.) Cloud‑Native Delivery: Building scalable AI pipelines on AWS or Azure; Terraform/IaC familiarity Python Excellence: Clean, testable code More ❯

Desirable Experience of Microsoft Fabric Experience of Microsoft Dynamics Experience with data governance and compliance frameworks Proficiency in AI/ML frameworks (TensorFlow, PyTorch, MLflow) Knowledge of Agile and DevOps methodologies Other Stuff Benefits include 25 Days Annual Leave, Medical Insurance, Flexible Hybrid/Home Working, 5% Employer Contribution Pension More ❯

PostgreSQL Databricks Containerisation: Docker, Kubernetes CI/CD: Azure DevOps, GitHub Actions Relational databases and data lake architecture Model and data pipeline integration (e.g. MLflow) Microsoft Azure (Functions, Storage, Compute) Monitoring tools (Grafana, Prometheus, etc.) Mentoring and knowledge sharing within the team Senior Engineer - Desirable Skills: Experience in energy supply More ❯

PostgreSQL Databricks Containerisation: Docker, Kubernetes CI/CD: Azure DevOps, GitHub Actions Relational databases and data lake architecture Model and data pipeline integration (e.g. MLflow) Microsoft Azure (Functions, Storage, Compute) Monitoring tools (Grafana, Prometheus, etc.) Mentoring and knowledge sharing within the team Senior Engineer - Desirable Skills: Experience in energy supply More ❯

lifecycle and agile methodologies. Proven experience designing, developing, and deploying machine learning models. Experience with debugging ML models. Experience with orchestration frameworks (e.g. Airflow, MLFlow, etc). Experience deploying machine learning models to production environments. Knowledge of MLOps practices and tools for model monitoring and maintenance. Familiarity with containerization and More ❯

version control, CI/CD pipelines, and test-driven development. Nice to Have: Experience within financial services or fintech. Familiarity with MLOps tools (e.g., MLflow, Kubeflow, SageMaker). Understanding of regulatory or compliance frameworks. Academic background in mathematics, statistics, or quantitative disciplines. Who You Are: A natural problem-solver and More ❯

version control, CI/CD pipelines, and test-driven development. Nice to Have: Experience within financial services or fintech. Familiarity with MLOps tools (e.g., MLflow, Kubeflow, SageMaker). Understanding of regulatory or compliance frameworks. Academic background in mathematics, statistics, or quantitative disciplines. Who You Are: A natural problem-solver and More ❯

technologies like AWS and Databricks to deliver scalable, robust solutions.. AI/ML Lifecycle Awareness : Familiarity with the machine learning lifecycle (e.g., tools like MLflow, Data Bricks Model Serving) and its integration into production systems. Collaboration : Strong interpersonal skills with the ability to work effectively across teams, including specialists in More ❯

with CI/CD pipelines and infrastructure-as-code tools (Terraform is a plus). Experience with Airflow or similar orchestration tools. Familiarity with MLflow or MLOps practices. Knowledge of data warehousing solutions (Snowflake, Redshift, BigQuery). Consulting background is a plus. Strong communication skills (oral & written) Rights to work More ❯

with CI/CD pipelines and infrastructure-as-code tools (Terraform is a plus). Experience with Airflow or similar orchestration tools. Familiarity with MLflow or MLOps practices. Knowledge of data warehousing solutions (Snowflake, Redshift, BigQuery). Consulting background is a plus. Strong communication skills (oral & written) Rights to work More ❯

large-scale models. Solid programming skills in Python and familiarity with machine learning frameworks like TensorFlow, PyTorch, Hugging Face Transformers, and MLOps tools (e.g., MLflow, Kubeflow). Strong analytical and problem-solving skills, with an aptitude for translating complex theoretical research into practical applications. Day to Day Conduct research and More ❯

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 More ❯

Evidently, etc.) Experience in building parallelised or distributed model inference pipelines Nice-to-Have Skills Familiarity with feature stores and model registries (e.g. Feast, MLflow, SageMaker Model Registry) Knowledge of model versioning , A/B testing , and shadow deployments Experience implementing or contributing to MLOps frameworks and scalable deployment patterns More ❯

PRA SS1/21, FCA PS21/3, DORA, NIST AI RMF). Experience working with AI/ML platforms and monitoring tools (e.g., MLflow , Azure ML, SageMaker , Databricks , Python etc ). Excellent stakeholder management and communication skills. Desirable: Technical background in AI/ML, data science, or software engineering More ❯

PRA SS1/21, FCA PS21/3, DORA, NIST AI RMF). Experience working with AI/ML platforms and monitoring tools (e.g., MLflow , Azure ML, SageMaker , Databricks , Python etc ). Excellent stakeholder management and communication skills. Desirable: Technical background in AI/ML, data science, or software engineering More ❯

PRA SS1/21, FCA PS21/3, DORA, NIST AI RMF). Experience working with AI/ML platforms and monitoring tools (e.g., MLflow , Azure ML, SageMaker , Databricks , Python etc ). Excellent stakeholder management and communication skills. Desirable: Technical background in AI/ML, data science, or software engineering More ❯

PRA SS1/21, FCA PS21/3, DORA, NIST AI RMF). Experience working with AI/ML platforms and monitoring tools (e.g., MLflow , Azure ML, SageMaker , Databricks , Python etc ). Excellent stakeholder management and communication skills. Desirable: Technical background in AI/ML, data science, or software engineering More ❯

PRA SS1/21, FCA PS21/3, DORA, NIST AI RMF). Experience working with AI/ML platforms and monitoring tools (e.g., MLflow , Azure ML, SageMaker , Databricks , Python etc ). Excellent stakeholder management and communication skills. Desirable: Technical background in AI/ML, data science, or software engineering More ❯

analysis. Expertise in Python and the tools and libraries that make ML magic happen. Familiarity with ML experiment tracking and collaboration tools, such as Mlflow and Weights & Biases. A solid background in software engineering and DevOps practices, MLOps deployment, and infrastructure. A knack for generative AI frameworks and SDKs, like More ❯

with a bias for action. What Technologies We Use: Python, dbtLabs, SQL, GitHub Actions (CI/CD) Terraform, Docker, MLOps Platform (VertexAI, Sagemaker and MLFlow) With ecobee, you'll have the opportunity to: Be part of something big: Get to work in a fresh, dynamic, and ever-growing industry. Make More ❯

e.g., PRA SS1/21, FCA PS21/3, DORA, NIST AI RMF). Experience with AI/ML platforms and monitoring tools (e.g., MLflow, Azure ML, SageMaker, Python). Excellent stakeholder management and communication skills. Desirable Skills: Technical background in AI/ML, data science, or software engineering. Experience More ❯