City of London, London, United Kingdom Hybrid/Remote Options
Experis UK
scikit-learn, pandas, NumPy). Solid understanding of machine learning algorithms , statistical modelling , and deep learning architectures . Hands-on experience with MLOps tools and frameworks (e.g., MLflow, Kubeflow, SageMaker, Vertex AI). Experience with data engineering concepts — ETL pipelines, data lakes, and cloud data platforms. Proficiency with cloud services (AWS, Azure, or GCP) for model deployment and orchestration. More ❯
scikit-learn, pandas, NumPy). Solid understanding of machine learning algorithms , statistical modelling , and deep learning architectures . Hands-on experience with MLOps tools and frameworks (e.g., MLflow, Kubeflow, SageMaker, Vertex AI). Experience with data engineering concepts — ETL pipelines, data lakes, and cloud data platforms. Proficiency with cloud services (AWS, Azure, or GCP) for model deployment and orchestration. More ❯
ML algorithms , NLP , deep learning , and statistical methods. Experience with Docker, Kubernetes , and cloud platforms like AWS/Azure/GCP . Hands-on experience with MLOps tools (MLflow, SageMaker, Kubeflow, etc.) and version control systems. Strong knowledge of APIs, microservices architecture, and CI/CD pipelines. Proven experience in leading teams, managing stakeholders, and delivering end-to-end More ❯
London, England, United Kingdom Hybrid/Remote Options
Revenir
to Artificial Intelligence, Machine Learning, or Natural Language Processing Familiarity with frameworks like Express.js, Flask, or NestJS Familiarity with libraries like TensorFlow, PyTorch, or scikit-learn Basic understanding of Amazon Web Services Culture: Coffee and snacks in the office Team socials Inclusive working environment, supporting all genders, sexualities, race, disability or background Join our dynamic team at Revenir and More ❯
experience with cloud-native development (GCP preferred). Hands-on experience with GCP Vertex AI (model endpoints, pipelines, embeddings, vector search) or equivalent cloud-native ML platforms (e.g. AWS SageMaker, Azure ML) and agent orchestration frameworks such as LangChain and LangGraph Solid understanding of MLOps - CI/CD, IaC (Terraform), experiment tracking, model registry, and monitoring. Proven experience deploying More ❯
science, machine learning, or analytics leadership. • Strong understanding of model lifecycle management, experimentation frameworks, and data science governance. • Familiarity with MLOps concepts and tooling (e.g., MLflow, Kubeflow, Vertex AI, SageMaker, Azure ML). • Hands-on experience with data science tools and languages such as Python, R, SQL, and relevant frameworks (e.g., scikit-learn, TensorFlow, PyTorch). • Proven ability to More ❯
science, machine learning, or analytics leadership. • Strong understanding of model lifecycle management, experimentation frameworks, and data science governance. • Familiarity with MLOps concepts and tooling (e.g., MLflow, Kubeflow, Vertex AI, SageMaker, Azure ML). • Hands-on experience with data science tools and languages such as Python, R, SQL, and relevant frameworks (e.g., scikit-learn, TensorFlow, PyTorch). • Proven ability to More ❯
City of London, London, United Kingdom Hybrid/Remote Options
LHH
or internal clients within large organisations) through RFI/RFP responses, bid documentation, and client presentations. Hands-on experience with data science platforms such as Databricks , Dataiku , AzureML , or SageMaker , and machine learning frameworks such as TensorFlow , Keras , PyTorch , and scikit-learn . Expertise in cloud platforms ( AWS , Azure , Google Cloud ) and experience deploying solutions using cloud provisioning tools More ❯
or internal clients within large organisations) through RFI/RFP responses, bid documentation, and client presentations. Hands-on experience with data science platforms such as Databricks , Dataiku , AzureML , or SageMaker , and machine learning frameworks such as TensorFlow , Keras , PyTorch , and scikit-learn . Expertise in cloud platforms ( AWS , Azure , Google Cloud ) and experience deploying solutions using cloud provisioning tools More ❯
science, machine learning, or analytics leadership. • Strong understanding of model lifecycle management, experimentation frameworks, and data science governance. • Familiarity with MLOps concepts and tooling (e.g., MLflow, Kubeflow, Vertex AI, SageMaker, Azure ML). • Hands-on experience with data science tools and languages such as Python, R, SQL, and relevant frameworks (e.g., scikit-learn, TensorFlow, PyTorch). • Proven ability to More ❯
science, machine learning, or analytics leadership. • Strong understanding of model lifecycle management, experimentation frameworks, and data science governance. • Familiarity with MLOps concepts and tooling (e.g., MLflow, Kubeflow, Vertex AI, SageMaker, Azure ML). • Hands-on experience with data science tools and languages such as Python, R, SQL, and relevant frameworks (e.g., scikit-learn, TensorFlow, PyTorch). • Proven ability to More ❯
developing and deploying ML models in production environments. Strong Python skills, with experience in frameworks like PyTorch, TensorFlow, or Hugging Face. Confident working in AWS or similar cloud environments (SageMaker, Lambda, Docker, etc.). Experienced in (or eager to explore) areas such as forecasting, optimisation, reinforcement learning, generative AI, or computer vision. Solid engineering mindset, you know how to More ❯
developing and deploying ML models in production environments. Strong Python skills, with experience in frameworks like PyTorch, TensorFlow, or Hugging Face. Confident working in AWS or similar cloud environments (SageMaker, Lambda, Docker, etc.). Experienced in (or eager to explore) areas such as forecasting, optimisation, reinforcement learning, generative AI, or computer vision. Solid engineering mindset, you know how to More ❯
City of London, London, United Kingdom Hybrid/Remote Options
Nexia
similar) Strong understanding of ML principles and model evaluation Experience with cloud-based model deployment (GCP preferred) Familiarity with containerised workflows (Docker, serverless) and MLOps tools (MLflow, Vertex AI, SageMaker) Excellent communication and collaboration skills in a remote-first team Willingness to travel occasionally for in-person collaboration or client work Nice to Have Experience with retrieval-augmented generation More ❯
similar) Strong understanding of ML principles and model evaluation Experience with cloud-based model deployment (GCP preferred) Familiarity with containerised workflows (Docker, serverless) and MLOps tools (MLflow, Vertex AI, SageMaker) Excellent communication and collaboration skills in a remote-first team Willingness to travel occasionally for in-person collaboration or client work Nice to Have Experience with retrieval-augmented generation More ❯
Ability to communicate complex analytical concepts clearly and effectively to a range of audiences. Experience with model governance, documentation, and deployment best practices. Experience with cloud environments (e.g., AWS Sagemaker). Experience with collaborative development tools (e.g., Git, JIRA) is a plus. Prior experience in financial services, banking, or credit risk modelling is beneficial. 5-8 years experience in More ❯
Ability to communicate complex analytical concepts clearly and effectively to a range of audiences. Experience with model governance, documentation, and deployment best practices. Experience with cloud environments (e.g., AWS Sagemaker). Experience with collaborative development tools (e.g., Git, JIRA) is a plus. Prior experience in financial services, banking, or credit risk modelling is beneficial. 5-8 years experience in More ❯
decisioning is strongly preferred Experience with model governance and monitoring in regulated environments Experience with cloud platforms (AWS, GCP, Azure), preferably AWS, ML tools such as the AWS suite: Sagemaker Key Details Reporting to Lead Data Scientist Hours Full time Location London - Hybrid WFH model, x2 days a week onsite (Wed/Thurs) Working with Yaspa We are a More ❯
City of London, London, United Kingdom Hybrid/Remote Options
Yaspa
decisioning is strongly preferred Experience with model governance and monitoring in regulated environments Experience with cloud platforms (AWS, GCP, Azure), preferably AWS, ML tools such as the AWS suite: Sagemaker Key Details Reporting to Lead Data Scientist Hours Full time Location London - Hybrid WFH model, x2 days a week onsite (Wed/Thurs) Working with Yaspa We are a More ❯
access control Strong understanding of security and compliance requirements in financial services (FCA, DORA, PRA) Familiarity with CI/CD pipelines (GitHub Actions, Jenkins, or CodePipeline) Desirable exposure to SageMaker , model lifecycle integration, and MLOps monitoring Role Overview You’ll be part of a growing platform engineering function building the next generation of secure AWS services. This position suits More ❯
City of London, London, United Kingdom Hybrid/Remote Options
Robert Half
access control Strong understanding of security and compliance requirements in financial services (FCA, DORA, PRA) Familiarity with CI/CD pipelines (GitHub Actions, Jenkins, or CodePipeline) Desirable exposure to SageMaker , model lifecycle integration, and MLOps monitoring Role Overview You’ll be part of a growing platform engineering function building the next generation of secure AWS services. This position suits More ❯
of the curve in Generative AI, ML infrastructure, and cloud-native tooling. Tech Stack Programming: Python (Java familiarity is a plus). AWS: S3, Kinesis, Glue, Lambda, Step Functions, SageMaker, and more. On-Prem: Managed Kubernetes Platform and Hadoop ecosystem. Why This Role is Dfferent Direct Impact: Build AI tools that traders and quants use daily to optimize strategies. More ❯
of the curve in Generative AI, ML infrastructure, and cloud-native tooling. Tech Stack Programming: Python (Java familiarity is a plus). AWS: S3, Kinesis, Glue, Lambda, Step Functions, SageMaker, and more. On-Prem: Managed Kubernetes Platform and Hadoop ecosystem. Why This Role is Dfferent Direct Impact: Build AI tools that traders and quants use daily to optimize strategies. More ❯
