21 of 21 Remote/Hybrid Permanent Amazon SageMaker Jobs

model choices to technical and non-technical stakeholders Develop clean, well-tested, production-ready Python code Build, train and deploy models on AWS using Amazon SageMaker and related services Tune model performance through hyperparameter optimisation, feature engineering and error analysis Instrument models in production, defining and tracking MLOps … learn), writing maintainable, production-quality code. AWS: Hands-on experience building, training and deploying ML models on the AWS stack, ideally with experience in Amazon SageMaker Model tuning & MLOps: Practical experience in hyperparameter tuning and performance optimisation. You can articulate the metrics you'd track for MLOps (e.g. ...

experience developing and deploying ML models in Python using frameworks such as scikit-learn, XGBoost, PyTorch, or TensorFlow Strong experience with AWS ML services (SageMaker, Lambda, S3) in production environments Strong experiment design skills: hypothesis formulation, A/B testing methodology, and statistical evaluation Proven track record transitioning models ...

experience developing and deploying ML models in Python using frameworks such as scikit-learn, XGBoost, PyTorch, or TensorFlow Strong experience with AWS ML services (SageMaker, Lambda, S3) in production environments Strong experiment design skills: hypothesis formulation, A/B testing methodology, and statistical evaluation Proven track record transitioning models ...

experience developing and deploying ML models in Python using frameworks such as scikit-learn, XGBoost, PyTorch, or TensorFlowStrong experience with AWS ML services (SageMaker, Lambda, S3) in production environmentsStrong experiment design skills: hypothesis formulation, A/B testing methodology, and statistical evaluationProven track record transitioning models from experimentation ...

experience developing and deploying ML models in Python using frameworks such as scikit-learn, XGBoost, PyTorch, or TensorFlow Strong experience with AWS ML services (SageMaker, Lambda, S3) in production environments Strong experiment design skills: hypothesis formulation, A/B testing methodology, and statistical evaluation Proven track record transitioning models ...

monitoring.Experience creating automated evaluations for generative AI applications.Experience with AWS, Docker, Kubernetes, Terraform, and continuous integration and deployment practices.Experience using services such as AWS SageMaker or AWS Bedrock.Experience with Kafka, vector databases, or other technologies used for real time and high dimensional data processing.Experience managing machine learning workflows using ...

automated evaluations for generative AI applications. Experience with AWS, Docker, Kubernetes, Terraform, and continuous integration and deployment practices. Experience using services such as AWS SageMaker or AWS Bedrock. Experience with Kafka, vector databases, or other technologies used for real time and high dimensional data processing. Experience managing machine learning ...

similar technologies.Strong experience designing and operating cloud systems using AWS, Docker, Kubernetes, Terraform, and continuous integration and deployment practices.Familiarity with services such as AWS SageMaker or AWS Bedrock.Experience with event-driven processing, vector databases, and machine learning lifecycle tools such as Kafka and MLflow, or comparable technologies.Experience establishing observability ...

Strong experience designing and operating cloud systems using AWS, Docker, Kubernetes, Terraform, and continuous integration and deployment practices. Familiarity with services such as AWS SageMaker or AWS Bedrock. Experience with event-driven processing, vector databases, and machine learning lifecycle tools such as Kafka and MLflow, or comparable technologies. Experience ...

least one major cloud provider. AWS, GCP or AzureFamiliar with Docker and CI/CD pipelinesExperience with managed ML platforms such as SageMaker, Vertex AI or Azure MLData engineering and MLOpsExperience with data warehouses such as Snowflake, BigQuery or RedshiftWorkflow orchestration using tools like Airflow or DagsterExperience with MLOps ...

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 ...

Collaborate with Data Scientists and Engineers to productionise models. Improve platform scalability, reliability, and automation. Key Skills Strong Python experience. MLOps tooling (MLflow, Kubeflow, SageMaker, or similar). Docker and Kubernetes. CI/CD tooling (GitHub Actions, GitLab CI, Azure DevOps). Cloud platforms (AWS, Azure, or GCP). ...

scikit, pytorch, tidyverse, caret, ggplot, etc.).Proven experience developing, refining, and monitoring NLP models.Familiarity with database management tools and techniques (e.g., SQL, Selenium, S3, Sagemaker, API protocols) is preferred but not required.Understanding model evaluation methods and metrics.Ability to operationalize non-technical ideas into relevant research designs, features, and model ...

tidyverse, caret, ggplot, etc.). Proven experience developing, refining, and monitoring NLP models. Familiarity with database management tools and techniques (e.g., SQL, Selenium, S3, Sagemaker, API protocols) is preferred but not required. Understanding model evaluation methods and metrics. Ability to operationalize non-technical ideas into relevant research designs, features ...

problem statements and figure out how to solve themSome extra skills that are great (but not essential): Experience with MLOps tools: Airflow, MLflow, AWS SageMaker, AWS S3, AWS EMR, CI/CDPrior experience in the fraud domain and a strong understanding of fraud detection techniquesExperience designing and deploying ...

/CD and a cloud platform, and debugging from logs and traces. Nice to haveAgents used daily by operators, with a feedback loop. AWS SageMaker, AWS ECS/Fargate, Terraform and CircleCI.Snowflake and dbt. Event logging for analytics or machine learning. Lead generation or ad tech. ...

libraries, frameworks and services : such as ML libraries e.g. TensorFlow or PyTorch; model servers such as AWS Bedrock; MLOps such as ML Flow or Sagemaker; agent/AI orchestration such as LangGraph or MCP libraries Strong programming skills, especially in Python (other languages like C++, Scala, Rust ...

engineering best practices , covering LLMOps, MLOps, CI/CD, automated evaluation, observability, security, performance and cost optimisation. Work extensively with AWS AI services , including Amazon Bedrock, Bedrock AgentCore, SageMaker, Lambda and Step Functions. What We Are Looking For: Proven experience as an Engineering Manager or Technical Lead with … agent frameworks such as LangGraph, Strands Agents or Claude Agent SDK, alongside interoperability standards including MCP and A2A. Strong AWS cloud engineering experience, particularly Amazon Bedrock, Bedrock AgentCore, SageMaker, Lambda and Step Functions, with Infrastructure as Code using AWS CDK or equivalent. Hands-on experience with LLMOps ...

Strands Agents, LangGraph or the Claude Agent SDK, and with interoperability standards such as MCP and A2A.Strong cloud-native expertise on AWS, especially Amazon Bedrock and Bedrock AgentCore, alongside SageMaker, Lambda and Step Functions, with CDK or equivalent IaC.Hands-on LLMOps and MLOps experience, including agent and trajectory … capabilities consumed by multiple product teams, such as internal MCP servers or agent tooling.Experience with vector search at scale, using tools such as OpenSearch, Amazon S3 Vectors or pgvector.Familiarity with AI governance and regulation, and with putting responsible AI practices into day-to-day engineering.An advanced degree in computer ...

Location: London; UKWork type: HybridSeniority: DirectorPrimary skill: Amazon Web ServicesPosted: 2026-09-29We're looking for a Head of AWS Technology to join our team in London in a hybrid working mode. This is a senior leadership role that combines technical depth, architectural authority, and commercial responsibility … sectors. ResponsibilitiesSet the technical vision, architecture standards and engineering practices for the AWS practiceLead adoption of AI and GenAI capabilities on AWS, including Bedrock, SageMaker and RAG patternsProvide hands-on technical oversight on complex engagements and ensure quality deliveryEstablish and nurture the AWS partnership through competencies, programs and joint ...

About the roleWe already run two kinds of agent alongside our teams. AI agents diagnose performance problems and tell people the root cause. Rule-based agents recommend marketing actions to our advertising teams. The quality ...