26 to 50 of 108 MLflow Jobs in the UK

Experience with Azure/GCP and AWS. Experience in automation of performance testing. Data environments exposure is a plus (Airflow, EMR, SageMaker, Ray, Tensorflow, MLflow, Kubeflow, Dask). Working conditions: Occasional out of hour's conferencing with overseas colleagues. Occasional out of hours or weekend work. A workplace that supports More ❯

tools. Ability to communicate complex ideas in machine learning to non-technical stakeholders. You may have: Experience with one or more ML Ops frameworks - MLFlow, Kubeflow, Azure ML, Sagemaker. Strong theoretical foundations in linear algebra, probability theory, or optimization. Experience and training in finance and operations domains. Deep experience with More ❯

to work outside your job description. Our tech stack Languages: Python (PyTorch, NumPy, OpenCV) ML Frameworks: PyTorch, TensorFlow, OpenCV Infrastructure: AWS, GCP, Docker, Kubernetes, MLFlow MLOps Tools: DVC, Weights & Biases, TensorRT Version Control & CI/CD: GitHub, GitLab, Jenkins Key Responsibilities Develop & optimise high-performance Computer Vision and deep learning More ❯

teams. Deep knowledge of media measurement techniques, such as media mix modelling. Experience with advanced AI techniques, including NLP, GenAI, and CausalAI. Familiarity with MLFlow, API design (FastAPI), and dashboard building (Dash). If this role looks of interest, reach out to Joseph Gregory More ❯

teams. Deep knowledge of media measurement techniques, such as media mix modelling. Experience with advanced AI techniques, including NLP, GenAI, and CausalAI. Familiarity with MLFlow, API design (FastAPI), and dashboard building (Dash). If this role looks of interest, reach out to Joseph Gregory More ❯

with unit and integration tests Strong understanding of machine learning algorithms and best practices Vision for MLOps best practices, particularly regarding version control, Docker, MLFlow, CI/CD Strong communication skills, with the ability to engage effectively with diverse stakeholders Good commercial understanding; knowledge of marketing operations is a bonus More ❯

decision-making processes Hands-on experience with AI/ML frameworks (TensorFlow, PyTorch), LLM orchestration tools (LangChain, LangGraph), MLOps practices and tooling (such as MLflow, Kubeflow, or similar), vector databases, and cloud platforms (AWS, Azure, GCP) with their AI/ML offerings Preferably hands-on experience with voice technologies and More ❯

NLP unstructured data. Implemented ML platform in enterprise client experience of ML use-cases AIML Platforms: DataRobot, Dataiku, Anaconda, Sagemaker, Vertex etc. AIML Frameworks: MLFlow, KubeFlow, BentoML, TensorFlow etc. AIML Development: PyTorch, Jupyter Notebooks, XGBoost, Tensorflow etc. AIML Models: Linear/Logistic Regression, KNN, Decision Trees, Anomaly Detection, LLMs, Generative More ❯

NLP unstructured data. Implemented ML platform in enterprise client experience of ML use-cases AIML Platforms: DataRobot, Dataiku, Anaconda, Sagemaker, Vertex etc. AIML Frameworks: MLFlow, KubeFlow, BentoML, TensorFlow etc. AIML Development: PyTorch, Jupyter Notebooks, XGBoost, Tensorflow etc. AIML Models: Linear/Logistic Regression, KNN, Decision Trees, Anomaly Detection, LLMs, Generative More ❯

Understanding of software development life cycle and agile methodologies. Proven experience designing, developing, and deploying machine learning models. Experience with orchestration frameworks (eg Airflow, MLFlow, etc) Experience deploying machine learning models to production environments. Knowledge of MLOps practices and tools for model monitoring and maintenance. Hands-on experience with cloud More ❯

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 More ❯

Data Science Applications. Proficient Python skills, including experience with relevant data libraries. Cloud engineering experience, particularly with AWS and Databricks.Exposure to GenAI/NLP, MLflow, Jenkins, workflow automation, AutoML, unit testing, and model explainability is a plus What You'll Do: As a Machine Learning Engineer, you will be a More ❯

model deployments. Proficient Python skills, including experience with relevant data libraries. Cloud engineering experience, particularly with AWS and Databricks. Exposure to GenAI/NLP, MLflow, Jenkins, workflow automation, AutoML, unit testing, and model. What You'll Do: As a Machine Learning Engineer, you will be a pivotal part of our More ❯

Experience with business intelligence tools like Tableau or PowerBI. Experience working with LLMs. Experience working with AWS Services like EC2, RDS(Postgres), SQS, Sagemaker, MLflow, S3, API gateway, ECS. Experience in UI frameworks like VueJS is a plus. About Us FactSet creates flexible, open data and software solutions for tens 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 ❯

while fostering an inclusive and high-performing culture. Strong hands-on background in deploying machine learning models at scale, using technologies such as Pytorch, MLflow, Airflow, and Docker. Deep understanding of ML lifecycle challenges: feature stores, model deployment, monitoring, data drift, and retraining. Familiarity with cloud platforms (ideally Azure), infrastructure 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 ❯

audiences Nice-to-Have: Experience with marketing data, customer-level modelling, or decision science (e.g. uplift, attribution, causal AI, optimization) Familiarity with MLOps tooling (MLflow, FastAPI, Airflow, etc.) Experience designing and interpreting A/B tests or other experimental frameworks Background in consulting, agency, or fast-paced environments where autonomy More ❯

audiences Nice-to-Have: Experience with marketing data, customer-level modelling, or decision science (e.g. uplift, attribution, causal AI, optimization) Familiarity with MLOps tooling (MLflow, FastAPI, Airflow, etc.) Experience designing and interpreting A/B tests or other experimental frameworks Background in consulting, agency, or fast-paced environments where autonomy More ❯