26 to 50 of 56 MLflow Jobs in the UK

partners. Nice to have Experience with quality weighted bidding, uplift modelling, or reinforcement style policy optimisation. Familiarity with MMM, MTA, and experiment design in marketing contexts. Vertex AI or MLflow for training and deployment. Containerisation and service reliability skills. Why join us Monthly long weekends: every third Friday off. Wellness stipend and comprehensive parental leave policies. Remote first culture with 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 ❯

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 ❯

MLOps Knowledge Experience taking models from experiments through to production deployments, with tools such as Docker, Kubernetes & serverless alternatives such as AWS Lambda. Familiarity with MLOps tools such as MLFlow, Kubeflow or Sagemaker. A strong knowledge of cloud platforms (ideally AWS) and their respective services for deploying robust, AI-heavy applications. Bonus Experience Experience with named entity recognition/recommendation More ❯

and monitoring in cloud environments (AWS). Understanding of machine learning lifecycle and data pipelines. Proficiency with Python, Pyspark, Big-data ecosystems Hands-on experience with MLOps tools (e.g., MLflow, Airflow, Docker, Kubernetes) Secondary Skills Experience with RESTful APIs and integrating backend services All profiles will be reviewed against the required skills and experience. Due to the high number of More ❯

and monitoring in cloud environments (AWS). Understanding of machine learning lifecycle and data pipelines. Proficiency with Python, Pyspark, Big-data ecosystems Hands-on experience with MLOps tools (e.g., MLflow, Airflow, Docker, Kubernetes) Secondary Skill : Experience with RESTful APIs and integrating backend services ABOUT CAPGEMINI Capgemini is a global business and technology transformation partner, helping organizations to accelerate their dual More ❯

and monitoring in cloud environments (AWS). Understanding of machine learning lifecycle and data pipelines. Proficiency with Python, Pyspark, Big-data ecosystems Hands-on experience with MLOps tools (e.g., MLflow, Airflow, Docker, Kubernetes) Secondary Skill : Experience with RESTful APIs and integrating backend services ABOUT CAPGEMINI Capgemini is a global business and technology transformation partner, helping organizations to accelerate their dual More ❯

and monitoring in cloud environments (AWS). Understanding of machine learning lifecycle and data pipelines. Proficiency with Python, Pyspark, Big-data ecosystems Hands-on experience with MLOps tools (e.g., MLflow, Airflow, Docker, Kubernetes) Secondary Skill : Experience with RESTful APIs and integrating backend services ABOUT CAPGEMINI Capgemini is a global business and technology transformation partner, helping organizations to accelerate their dual More ❯

and act on. Key requirements: MSc or BSc in Computer Science, Data Science, Bioinformatics, Engineering, or a related field, or equivalent experience. Proven experience designing and deploying MLOps pipelines (MLflow, Azure ML, Azure DevOps etc). Strong programming skills in Python and familiarity with common ML/AI libraries (scikit-learn, tensorflow, Keras etc.). Experience implementing machine learning and More ❯

of cutting-edge techniques for Natural Language Processing and Computer Vision Strong grasp of basic probability concepts and machine learning lifecycle Experience with workflow and pipelining frameworks (e.g., Kubeflow, MLFlow, Argo) Understanding and application of Ethical AI considerations Ready to take your career to the next level? Apply today and be part of something extraordinary! Please either apply by clicking More ❯

in programming languages such as Python or R, with extensive experience with LLMs, ML algorithms, and models. Experience with cloud services like Azure ML Studio, Azure Functions, Azure Pipelines, MLflow, Azure Databricks, etc., is a plus. Experience working in Azure/Microsoft environments is considered a real plus. Proven understanding of data science methods for analyzing and making sense of More ❯

globally distributed group tackling innovative R&D challenges that directly impact customers. Our Technology Stack: Python Agentic tools (Autogen, Semantic Kernel, LangChain) SQL MongoDB Third-party LLM APIs LiteLLM MLflow Google Cloud Platform Docker PyTorch Hugging Face Transformers SPARQL Kubernetes WHAT YOU WILL BE DOING AT BEAMERY Strategic AI Leadership Define and implement the company's AI and data science More ❯

complex business problems into actionable data science projects with clear KPIs and ROI. Technical Leadership: Mentor junior data scientists, establish best practices for code (e.g., Git), model versioning (e.g., MLflow), and reproducible research. Conduct code reviews and champion a culture of technical excellence. MLOps & Productionisation: Work with ML Engineers and DevOps teams to build robust, scalable, and automated MLOps pipelines … or computer vision. Big Data Tools: Experience with big data platforms like Spark (PySpark) for handling large-scale datasets. MLOps: Familiarity with MLOps tools and concepts (e.g., Docker, Kubernetes, MLflow, Airflow) for model deployment and lifecycle management. Financial Domain Knowledge: Direct experience with at least two of the following domains: Credit Risk Modeling, Fraud Detection, Anti-Money Laundering (AML), Know More ❯

required: AWS Data/ML Engineering & ML Ops (ECS, Sagemaker) CI/CD pipelines (GitLab, Jenkins) Python, PySpark & Big Data ecosystems AI/ML lifecycle, deployment & monitoring MLOps tooling (MLflow, Airflow, Docker, Kubernetes) Front-end exposure (HTML, Flask, Streamlit) RESTful APIs & backend integration If this ML Engineer role is of interest, please apply now for immediate consideration. More ❯

A/B testing Experiment design and hypothesis testing MLOps & Engineering Scalable ML systems (batch and real-time) ML pipelines, CI/CD, monitoring, deployment Familiarity with tools like MLflow, Kubeflow, Airflow, Docker, Kubernetes Strategic skills Align ML initiatives with business goals Prioritize projects based on ROI, feasibility, and risk Understand market trends and competitive ML strategies Communicate ML impact More ❯

Experience Academic background (research Masters level) or industry experience in a relevant field Strong experience managing on premise Kubernetes clusters Deep knowledge of Kubeflow or similar systems such as MLflow Proficient in Python and experienced with Linux systems Familiar with AWS services such as Cognito, S3, EC2 and Lambda Experience working with ML frameworks such as PyTorch or Lightning Capable More ❯

Git) and testing. Collaborative spirit and a playful can-do attitude. Persistent with a well-rounded view on creating solutions. Experience with tools and platforms such as Databricks and MLflow for ML training and experimenting is highly desirable.Applications are reviewed on an ongoing basis. However, please note we do amend or withdraw our jobs and reserve the right to do More ❯

channels. What you’ll need: • Python, SQL, and data modelling expertise • Advanced knowledge of Snowflake & Snowpark • Confident communicator who can influence and collaborate • Experience building and managing ML environments (MLflow or similar) • Familiarity with CI/CD practices What you’ll do: • Architect and implement scalable ML environments with Data Science • Define best practices for deployment, monitoring, and governance • Build More ❯

channels. What you’ll need: • Python, SQL, and data modelling expertise • Advanced knowledge of Snowflake & Snowpark • Confident communicator who can influence and collaborate • Experience building and managing ML environments (MLflow or similar) • Familiarity with CI/CD practices What you’ll do: • Architect and implement scalable ML environments with Data Science • Define best practices for deployment, monitoring, and governance • Build More ❯

channels. What you’ll need: • Python, SQL, and data modelling expertise • Advanced knowledge of Snowflake & Snowpark • Confident communicator who can influence and collaborate • Experience building and managing ML environments (MLflow or similar) • Familiarity with CI/CD practices What you’ll do: • Architect and implement scalable ML environments with Data Science • Define best practices for deployment, monitoring, and governance • Build More ❯

channels. What you’ll need: • Python, SQL, and data modelling expertise • Advanced knowledge of Snowflake & Snowpark • Confident communicator who can influence and collaborate • Experience building and managing ML environments (MLflow or similar) • Familiarity with CI/CD practices What you’ll do: • Architect and implement scalable ML environments with Data Science • Define best practices for deployment, monitoring, and governance • Build More ❯

channels. What you’ll need: • Python, SQL, and data modelling expertise • Advanced knowledge of Snowflake & Snowpark • Confident communicator who can influence and collaborate • Experience building and managing ML environments (MLflow or similar) • Familiarity with CI/CD practices What you’ll do: • Architect and implement scalable ML environments with Data Science • Define best practices for deployment, monitoring, and governance • Build More ❯

Proven track record of leading complex AI projects in an agile environment Strong technical proficiency with modern ML tools and frameworks: PyTorch, TensorFlow, SparkMLLib, SciPy, Scikit-Learn, NLTK (etc) MLflow or similar ML lifecycle tools Experience with AI governance and responsible AI practices Understanding of public sector data requirements and compliance Outstanding communication and stakeholder management skills Demonstrated ability to More ❯

personalised experiences across global brands and channels. What you'll need Python, SQL, data modelling Expertise in Snowflake & Snowpark Immaculate communication to influence and collaborate Experience building ML environments (MLflow or similar) CI/CD practices What you'll do Design and implement a scalable ML environment with Data Science Define best practice for deployment, monitoring, and governance Build pipelines More ❯

LangChain/LangGraph, LlamaIndex Experience with Hugging Face and LoRA/QLoRA for fine-tuning Experience with RAG & Vector DBs eg. FAISS, Weaviate, Pinecone Any experience of MLOps with MLFlow, AWS (SageMaker), CI/CD (GitHub Actions) or similar would be a benefit to an application The employer is well known not only for the forward-thinking approach they have More ❯