26 to 50 of 111 MLflow Jobs in England

years leading Databricks-based solutions. Proven experience in a consulting environment delivering large-scale data platform projects. Hands-on expertise in Spark, Delta Lake, MLflow, Unity Catalog, and DBSQL. Strong proficiency in Python, SQL, and at least one major cloud platform (AWS, Azure, or GCP). Excellent communication skills and 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 ❯

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 ❯

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 ❯

/GCP. · Ability to manage cloud infrastructure to ensure high availability, scalability, and cost efficiency. Nice-to-Have · Experience with ML orchestration platforms like MLflow, SageMaker Pipelines, Kubeflow, or similar. · Familiarity with model quantization, pruning, or other performance optimization techniques. · Exposure to distributed training frameworks like Unsloth, DeepSpeed, Accelerate, or 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 ❯

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 ❯

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 ❯

. This role is ideal for someone who enjoys blending technical precision with innovation. You’ll: Build and manage ML pipelines in Databricks using MLflow, Delta Lake, Spark, and Mosaic AI. Train and deploy generative models (LLMs, GANs, VAEs) for NLP, content generation, and synthetic data. Architect scalable solutions using 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 ❯

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 ❯

with marketing data or customer-level modelling (e.g., uplift, attribution, causal AI, graph AI, campaign optimization, spend optimization). Exposure to MLOps tools like MLflow, FastAPI, Airflow, or similar. Experience with experimentation and validation frameworks (e.g., A/B testing). Startup or freelance experience that required pace, clarity, and 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 ❯

grow. Experience in the following would be beneficial: Experience implementing model governance e.g. model versioning, drift reporting etc. Experience with MLOps tools such as MLFlow, Kubeflow, or DVC. Experience with distributed processing systems like Spark (Scala and PySpark would be invaluable). Experience with LLMs, and/or RAG architecture. 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 ❯

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 ❯

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 ❯

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 ❯

deployment. Proficiency with relevant ML libraries and frameworks such as PyTorch, TensorFlow, scikit-learn, HuggingFace or similar. Experience with modern ML tooling, such as MLflow, Jupyter, feature stores, and vector databases. Understanding of software engineering best practices including version control, testing, CI/CD, containerisation, and observability. Familiarity with MLOps More ❯