1 to 25 of 30 Permanent Amazon SageMaker Jobs in London

delivery of a production-grade machine learning platform on AWS. What you’ll be doing Design and maintain a production MLOps platform on Amazon SageMaker (Studio, Training, Pipelines, Endpoints) — including model registry, automated retraining, drift monitoring, and governance gates Lead the migration of a 12-model production suite … e.g., the CVM suite) from legacy infrastructure to SageMaker, owning parity testing methodology and sign-off Build and maintain CI/CD pipelines (CodePipeline/CodeBuild or equivalent) for automated model promotion across environments Define and enforce IAM least-privilege policies, KMS key management, and VPC/PrivateLink network ...

Docker, Kubernetes) to operate, support, and secure mission-critical applications Preferred qualifications, capabilities and skills Experience with MLOps tools and platforms such as MLflow, Amazon SageMaker, Google VertexAI, Databricks, BentoML, KServe, and Kubeflow Exposure to cloud-native microservices architecture Familiarity with advanced AI/ML concepts and protocols ...

should demonstrate experience across: Enterprise and solution architecture within complex organisations Cloud-based AI/ML platforms including Azure OpenAI, Azure ML, AWS Bedrock, SageMaker, Google Vertex AI, Databricks, Snowflake, Dataiku or similar technologies Designing scalable and cost-optimised AI solutions using serverless, containerised and Kubernetes-based environments Generative ...

serving: NVIDIA Triton, vLLM, TensorRT-LLM, or equivalent high-throughput serving platforms. MLOps/LLMOps: Kubeflow, MLflow, and at least one hyperscaler ML platform (SageMaker, Azure ML, or Vertex AI). Generative AI: LLM fine-tuning (LoRA/QLoRA), RAG architecture design, vector databases (Pinecone, Milvus, Weaviate), and agentic ...

production deployment, including MLOps, monitoring and drift detection. Practical knowledge of ML frameworks (e.g. scikit‐learn, PyTorch, XGBoost) and cloud‐native ML services (SageMaker, Azure ML, Vertex AI). Architecture and delivery of scalable cloud data and AI platforms on AWS, Azure or GCP; experienced with containerisation, IaC, event ...

. Hands‐on experience with GCP Vertex AI (model endpoints, pipelines, embeddings, vector search) or equivalent cloud‐native AI/ML platforms (e.g. AWS SageMaker, AzureML) and agent orchestration frameworks (e.g. LangChain, LangGraph, ADK). Solid understanding of MLOps – CI/CD, IaC (Terraform), experiment tracking, model registry ...

tooling exposure (LangChain, LlamaIndex, vector databases such as Pinecone, Weaviate or pgvector, RAG patterns, fine-tuning workflows) MLOps platforms (MLflow, Kubeflow, Vertex AI, SageMaker, Databricks or equivalent) Cloud experience (AWS, GCP, Azure), containerisation and basic data engineering/SQL Familiarity with experimentation, A/B testing and online evaluation ...

equivalents in Azure or GCP), with hands‐on work across classical ML and modern LLM/RAG systems using services like Amazon SageMaker and Bedrock. What you’ll do Partner with stakeholders across the business to explore high‐impact opportunities. Own the full lifecycle: problem framing, data discovery … building RAG systems. Orchestration: for example LangChain for pipelines/agents. RAG best practices and evaluation workflows (e.g., agentic/RAG patterns on SageMaker). Comfortable choosing the right technique for the job (from baselines to advanced models), with an emphasis on measurable impact and maintainability. Clear communication with ...

using Docker and Kubernetes. Engineering: Strong software engineering fundamentals, including version control, testing, and API/microservices architecture. Experience with MLOps platforms (e.g., MLflow, SageMaker) or agentic AI frameworks. Familiarity with secure development practices or regulated/high-compliance environments. Background in NLP, computer vision, or data engineering platforms. ...

prompt optimization frameworks (AutoPrompt, DSPy) and building evaluation suites. Hands-on experience with agentic frameworks (LangChain, LangGraph). Experience with AWS AI deployment services (SageMaker, Bedrock) and workflow orchestration. Demonstrated experience in financial services, particularly investment banking operations. Join us to help shape the future of Market Operations through ...

documentation and collaborate across engineering teams. Ecosystem Knowledge : Hands‐on experience supporting software delivery and MLOps tooling (e.g., Kubernetes, Kubeflow, MLflow, Triton, Docker, or SageMaker/Azure ML). Equal Opportunity Statement We are committed to fostering and promoting an inclusive professional environment for all of our employees ...

documentation and collaborate across engineering teams. Ecosystem Knowledge : Hands-on experience supporting software delivery and MLOps tooling (e.g., Kubernetes, Kubeflow, MLflow, Triton, Docker, or SageMaker/Azure ML) Equal Opportunity Statement We are committed to fostering and promoting an inclusive professional environment for all of our employees ...

Familiarity with cloud‐based application development (AWS & Azure). Familiarity with one or more ML frameworks and technologies: scikit‐learn, xgboost, TensorFlow, PyTorch, Spark, SageMaker, Vertex AI, Kubeflow, Seldon, Triton. Strong communication skills, able to express ideas clearly and collaborate across teams. #J-18808-Ljbffr ...

learning and use of AI to support those. Working knowledge of data platforms such as Snowflake and Databricks; ML platforms such as Azure ML, Amazon SageMaker, Google Vertex, etc.; as well as R and/or Python Familiarity with security protocols, application integration, embedding and verifying AI generated ...

with the Data Engineering and AI Platform teams, you will drive excellence and pace in the build, deployment, testing, and monitoring of models using Amazon Sagemaker [and occasionally Snowflake] Causal Inference & Decision Science: Lead the development of advanced causal models (e.g., Media Mix Modelling, retention drivers, and simulation ...

expand from a GCP‐first environment to a hybrid AWS and GCP architecture. Define GPU compute strategy across Kubernetes, Vertex AI and SageMaker, balancing performance, scalability and cost efficiency. Drive the production adoption of Generative AI and LLM capabilities, establishing best practices for evaluation, deployment, experimentation and governance. Collaborate ...

content classification, synthetic panels, model validation). Experience with data platforms such as Databricks, snowflake or other cloud native solutions (GCP big query, AWS sagemaker). Solid understanding of agile practices. Any experience in SAFe is considered a plus. How we work Fifty5Blue’s core values are: Clarity, Independence ...

models into production, balancing quick wins with robust engineering. Focus areas include AI/OCR workflows, RAG systems, and deploying models on AWS with SageMaker/Bedrock. This hybrid role requires 1–2 days in Central London per week and right to work in the UK. #J-18808-Ljbffr ...

models influence real lending decisions and commercial outcomes Experience deploying and maintaining models within production environments Strong Python, SQL and AWS experience (particularly SageMaker and Lambda) Comfortable working in a fast-moving, hands-on environment with high ownership Excellent communication skills with both technical and business stakeholders What ...

demonstrate excellent technical capabilities, including proficiency in Python, SQL and Git. Practical experience deploying machine learning models within cloud-based environments, particularly using AWS SageMaker, will be highly advantageous. Candidates should possess a solid understanding of statistical modelling techniques and demonstrate a genuine enthusiasm for machine learning, predictive analytics ...

models including scorecards, behavioural, acquisition and fraud models Build predictive models using advanced statistical and machine learning techniques Deploy models into production using AWS SageMaker and collaborate with engineering teams Monitor model performance and ensure ongoing effectiveness across business and technical metrics Generate insights and present clear, actionable recommendations ...

provide technical leadership across the full lifecycle of machine learning systems while architecting production-grade ML solutions. You will work with AWS SageMaker, lead teams on MLOps strategies, and ensure systems adhere to governance standards. This is a hands-on, technical position ideal for those with deep expertise ...

ground up that create lasting leverage across the entire engineering organisation Work at the Frontier: Get hands‐on with the latest AWS capabilities, including SageMaker Unified Studio, Bedrock, and beyond, in a stack that’s modern by design, not by accident Own It End‐to‐End: From … this role has real scope. No tickets, no hand‐offs, just ownership What You Bring AWS Depth: Strong production experience with Lambda, ECS, S3, SageMaker, and Bedrock, with solid knowledge of IAM, cloud networking, and security fundamentals Terraform Expertise: Deep, hands‐on Terraform experience. You write infrastructure‐as‐code ...

model monitoring, content safety, LLMOps, GenAIOps or production support for AI systems Experience with non‐Microsoft AI platforms or services, such as AWS Bedrock, Amazon Q, SageMaker, Google Vertex AI, Anthropic Claude, OpenAI APIs or open‐source model ecosystems Familiarity with ISO 42001, NIST ...

models and drive business insights.Be a Databricks champion or have some Databricks certificationsAWSWork hands-on with AWS services such as Redshift, Glue, Lake Formation, SageMaker, Athena, and moreStrong hands-on expertise with AWS services (e.g. Redshift, Glue, Lake Formation, SageMaker).AWS Certified ArchitectAdditional InfoSecurity Clearance: To be successfully ...