1 to 25 of 31 Permanent MLflow Jobs in London

in Python and ML/DL libraries (NumPy, Pandas, scikit-learn). Proven track record of building and deploying ML models in production environments. Knowledge of MLOps tools (e.g., MLflow, Kubeflow, Docker, Kubernetes, or similar). Experience working with cloud platforms (AWS, Azure, or GCP). Solid understanding of CNNs, object detection, segmentation, and image classification. Strong problem-solving skills More ❯

in Python and ML/DL libraries (NumPy, Pandas, scikit-learn). Proven track record of building and deploying ML models in production environments. Knowledge of MLOps tools (e.g., MLflow, Kubeflow, Docker, Kubernetes, or similar). Experience working with cloud platforms (AWS, Azure, or GCP). Solid understanding of CNNs, object detection, segmentation, and image classification. Strong problem-solving skills More ❯

in Python and ML/DL libraries (NumPy, Pandas, scikit-learn). Proven track record of building and deploying ML models in production environments. Knowledge of MLOps tools (e.g., MLflow, Kubeflow, Docker, Kubernetes, or similar). Experience working with cloud platforms (AWS, Azure, or GCP). Solid understanding of CNNs, object detection, segmentation, and image classification. Strong problem-solving skills More ❯

in Python and ML/DL libraries (NumPy, Pandas, scikit-learn). Proven track record of building and deploying ML models in production environments. Knowledge of MLOps tools (e.g., MLflow, Kubeflow, Docker, Kubernetes, or similar). Experience working with cloud platforms (AWS, Azure, or GCP). Solid understanding of CNNs, object detection, segmentation, and image classification. Strong problem-solving skills More ❯

learn, PyTorch, TensorFlow, and XGBoost. Design robust data pipelines using tools like Spark and Kafka for real-time and batch processing. Manage ML lifecycle with tools such as Databricks , MLflow , and cloud-native platforms (Azure preferred). Collaborate with engineering teams to ensure scalable, secure ML infrastructure aligned with compliance standards (e.g., ISO27001). Ensure data governance, particularly around sensitive More ❯

Python and key ML libraries (e.g. PyTorch, PySpark, scikit-learn, Hugging Face Transformers). Hands-on experience with modern data platforms and AI tooling such as Azure ML, Databricks, MLflow, LangChain, LangGraph. Proven experience with modern engineering practices Git, version control, unit testing and containerisation. Familiarity with agile work methodologies and tools like Jira and Confluence. Behavioural Attributes and Skills More ❯

Python and key ML libraries (e.g. PyTorch, PySpark, scikit-learn, Hugging Face Transformers). Hands-on experience with modern data platforms and AI tooling such as Azure ML, Databricks, MLflow, LangChain, LangGraph. Proven experience with modern engineering practices Git, version control, unit testing and containerisation. Familiarity with agile work methodologies and tools like Jira and Confluence. Behavioural Attributes and Skills More ❯

Python and key ML libraries (e.g. PyTorch, PySpark, scikit-learn, Hugging Face Transformers). Hands-on experience with modern data platforms and AI tooling such as Azure ML, Databricks, MLflow, LangChain, LangGraph. Proven experience with modern engineering practices Git, version control, unit testing and containerisation. Familiarity with agile work methodologies and tools like Jira and Confluence. Behavioural Attributes and Skills More ❯

Python and key ML libraries (e.g. PyTorch, PySpark, scikit-learn, Hugging Face Transformers). Hands-on experience with modern data platforms and AI tooling such as Azure ML, Databricks, MLflow, LangChain, LangGraph. Proven experience with modern engineering practices Git, version control, unit testing and containerisation. Familiarity with agile work methodologies and tools like Jira and Confluence. Behavioural Attributes and Skills More ❯

Python proficiency and expertise with frameworks like PyTorch, TensorFlow, Hugging Face, or LangChain. MLOps and Deployment: Experience with containerization tools (Docker, Kubernetes) and workflow management tools (Azure ML Studio, MLFlow). Cloud and AI Infrastructure: Hands-on experience with (preferably Azure) Cloud environments for scalable AI deployment, monitoring, and optimization. Document Processing and knowledge extraction tools. Databases: Experience with relational More ❯

of neural network architectures (CNNs, RNNs, Transformers, etc.). Experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes). Familiarity with MLOps practices and tools (e.g., MLflow, DVC, Airflow). Strong problem-solving skills and ability to work independently in a fast-paced environment. Preferred Qualifications: MSc or PhD in Computer Science, Machine Learning, or related field. More ❯

of neural network architectures (CNNs, RNNs, Transformers, etc.). Experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes). Familiarity with MLOps practices and tools (e.g., MLflow, DVC, Airflow). Strong problem-solving skills and ability to work independently in a fast-paced environment. Preferred Qualifications: MSc or PhD in Computer Science, Machine Learning, or related field. More ❯

of neural network architectures (CNNs, RNNs, Transformers, etc.). Experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes). Familiarity with MLOps practices and tools (e.g., MLflow, DVC, Airflow). Strong problem-solving skills and ability to work independently in a fast-paced environment. Preferred Qualifications: MSc or PhD in Computer Science, Machine Learning, or related field. More ❯

of neural network architectures (CNNs, RNNs, Transformers, etc.). Experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes). Familiarity with MLOps practices and tools (e.g., MLflow, DVC, Airflow). Strong problem-solving skills and ability to work independently in a fast-paced environment. Preferred Qualifications: MSc or PhD in Computer Science, Machine Learning, or related field. More ❯

Experience with Google Cloud Platform and/or Azure Cloud Services (DataFactory, Functions, SSIS) Hands-on experience with Databricks (GCP or Azure) Experience deploying and maintaining ML models (e.g., MLflow, Vertex AI, Azure ML) Beneficial Experience with Spark and other distributed data processing frameworks Exposure to MLOps tooling for orchestration, CI/CD, and monitoring Experience with Elasticsearch or similar More ❯

technologies. Preferred Qualifications: AWS certifications (e.g., Solutions Architect Associate/Professional, DevOps Engineer). Experience deploying and maintaining data processing and machine learning workloads. Familiarity with MLOps tools (e.g., MLflow, Airflow, BentoML). Knowledge of distributed systems, caching strategies, content delivery networks (CDNs), and load balancing. Understanding of Zero Trust Network (ZTN) concepts using Cloudflare. Basic experience with database administration More ❯

Implement rigorous code quality and testing standards across data science projects Support talent acquisition and continuous learning initiatives Knowledge and Experience Knowledge of ML model development and deployment frameworks (MLFlow, Kubeflow Advanced data querying (SQL) and data engineering pipelines (Airflow Extensive experience with comprehensive unit testing, integration testing, and test coverage strategies Experience working with Product Management teams and ability More ❯

the following technologies : Azure, AWS, GCP Airflow, dbt, Databricks, Snowflake, etc. GitHub, Azure DevOps and related developer tooling and CI/CD platforms, Terraform or other Infra-as-Code MLflow, AzureML or similar for MLOps; LangSmith, Langfuse and similar for LLMOps The difference to our "AI Engineer" role is: Do you "use/consume" these technologies, or are you the More ❯

record delivering production-grade ML models Solid grasp of MLOps best practices Confident speaking to technical and non-technical stakeholders 🛠️ Tech you’ll be using: Python, SQL, Spark, R MLflow, vector databases GitHub/GitLab/Azure DevOps Jira, Confluence 🎓 Bonus points for: MSc/PhD in ML or AI Databricks ML Engineer (Professional) certified More ❯

record delivering production-grade ML models Solid grasp of MLOps best practices Confident speaking to technical and non-technical stakeholders 🛠️ Tech you’ll be using: Python, SQL, Spark, R MLflow, vector databases GitHub/GitLab/Azure DevOps Jira, Confluence 🎓 Bonus points for: MSc/PhD in ML or AI Databricks ML Engineer (Professional) certified More ❯

in cloud environments, with an emphasis on scalability, code clarity, and long-term maintainability Hands-on experience with Databricks and/or Spark, especially Delta Lake, Unity Catalog, and MLflow Deep familiarity with cloud platforms, particularly AWS and Google Cloud Proven ability to manage data architecture and production pipelines in a fast-paced environment Track record of leading or independently More ❯

analytics, or data science. Hands-on familiarity with modern data stacks – SQL, dbt, Airflow, Snowflake, Looker/Power BI. Understanding of the AI/ML lifecycle – including tooling (Python, MLflow) and best-practice MLOps. Comfortable working across finance, risk, and commercial functions. Experience operating in a regulated environment, ideally with exposure to dual regulation and resilience frameworks. Please note - this More ❯

analytics, or data science. Hands-on familiarity with modern data stacks – SQL, dbt, Airflow, Snowflake, Looker/Power BI. Understanding of the AI/ML lifecycle – including tooling (Python, MLflow) and best-practice MLOps. Comfortable working across finance, risk, and commercial functions. Experience operating in a regulated environment, ideally with exposure to dual regulation and resilience frameworks. Please note - this More ❯

past, ideally centred around a software product, and have solid Python coding skills, and expertise with cloud infrastructure (preferably AWS). Familiarity with Containers and MLE tools such as MLflow and Airflow is essential, with any knowledge of AI SaaS or GenAI APIs being is a bonus. But what truly matters is your passion for learning and advancing technology. In More ❯

environment. Preferred Experience: Solid understanding of cyber security concepts such as threat detection, SIEM, anomaly detection, and incident response. Experience with tools for tracking ML models in production (e.g., MLflow). We encourage you to apply even if your experience is not a 100% match with the position. We are looking for someone with relevant skills and experience, not a More ❯