15 of 15 Permanent Amazon SageMaker Jobs in London

Data Pipeline Engineering Vector Databases such as Pinecone, Weaviate or ChromaDB Cloud & DevOps Experience with at least one major cloud platform: AWS (preferred) Bedrock SageMaker Lambda ECS/EKS S3 Or experience within: Microsoft Azure Google Cloud Platform (GCP) Additional experience with: Docker Kubernetes Git CI/CD Pipelines ...

distributed teams Continuous learning mindset with a focus on emerging technologies and innovation Nice-to-Have Experience with AWS or GCP ML platforms (e.g., SageMaker, Vertex AI) Front‐end development (React) or backend development (.NET/C#) Commercial awareness and understanding of business value delivery #J-18808-Ljbffr ...

technologies like docker, Kubernetes, AWS EKS etc. Knowledge of popular Cloud computing vendor (AWS and Azure) infrastructure & services e.g. AWS Bedrock,AWS S3, AWS Sagemaker, Azure AI search, Azure OpenAI, Azure blob storage etc. Master's degree or above in Machine learning/data science, computer science, applied mathematics ...

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 ...

Ecosystems: Solid understanding of ML algorithms, FastAPI, PyTorch/TensorFlow, MLflow, MLOps practices, containerization (Docker, Kubernetes/AWS EKS), and cloud services (AWS Bedrock, SageMaker, Azure AI Search). Soft Skills: Excellent communication and collaboration skills, with the ability to translate complex technical concepts for diverse, cross-functional stakeholders. ...

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 ...

Expertise with machine learning frameworks and data science packages (e.g., PyTorch, TensorFlow, Scikit‐Learn, NumPy, Pandas, SciPy, statsmodels). Experience deploying to AWS (e.g., SageMaker, Bedrock) and operating production workloads with attention to cost, performance, security, and scaling. Experience integrating human-in-the-loop or user feedback signals into ...

offs. Real-time ML & reproducibility: A strong grasp of production inference patterns and reproducible environments (Docker, MLflow or equivalent). Cloud & tooling: AWS, plus SageMaker, Snowflake, Spark/Databricks and Kubernetes. Engineering mindset: A focus on reliability, maintainability and continuous improvement. ...

Tungsten Automation solutions Production AI Experience : Leverage experience with deploying, monitoring, and maintaining AI/LLM models in production (e.g., Azure ML, AWS Sagemaker, or Kubernetes-based ML pipelines) Thought Leadership : Provide thought leadership on AI-driven automation trends and best practices, both internally and externally Collaboration and Cross ...

seen as theoretical 'black boxes' You will understand the tools and systems used to deliver all model activities, including the DSML tools (AWS Sagemaker),Snowflake and collaborate and advocate for the right approach as the needs of the business and technology evolves You will work in collaborative relationships ...

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 ...

forward-thinking team. What You Bring to the Team: Architectural Expertise: Proven track record in designing, deploying, and maintaining production-grade, distributed ML systems (Sagemaker) Deep MLOps Proficiency: Expert-level knowledge of CI/CD, orchestration (e.g., Apache Airflow, Flink), and model monitoring/drift detection at scale. Software … learning principles (experimental design, statistical distributions and test, machine learning algorithms) Expertise in deploying ML models at scale on AWS, with experience using MLFlow, Sagemaker or similar platforms. Strong Python programming skills and adherence to software engineering best practices (e.g., clean code, version control, code reviews, using Docker, Terform ...

forward-thinking team. What You Bring to the Team: Architectural Expertise: Proven track record in designing, deploying, and maintaining production-grade, distributed ML systems (Sagemaker) Deep MLOps Proficiency: Expert-level knowledge of CI/CD, orchestration (e.G., Apache Airflow, Flink), and model monitoring/drift detection at scale. Software … learning principles (experimental design, statistical distributions and test, machine learning algorithms) Expertise in deploying ML models at scale on AWS, with experience using MLFlow, Sagemaker or similar platforms. Strong Python programming skills and adherence to software engineering best practices (e.G., clean code, version control, code reviews, using Docker, Terform ...

design authority Comfortable working directly with senior client stakeholders Strong mentoring and coaching capability Experience with some of the following technologies or ecosystems: Amazon Bedrock and SageMaker Claude/Anthropic Azure AI Foundry or Azure OpenAI Databricks Vector databases and RAG architectures Agentic AI orchestration frameworks ...

Role Summary We are seeking a Principal Machine Learning Engineer (SageMaker, MLOps, Model Governance & Explainability) to provide technical leadership across the full lifecycle of machine learning systems powering a new matching platform. This role is accountable for defining ML architecture, establishing engineering standards, driving MLOps maturity, and ensuring that … platform, including data pipelines, model training workflows, inference runtimes, and telemetry ecosystems. Lead adoption of best‐in‐class MLOps patterns, platform tooling, and AWS SageMaker capabilities across training, processing, registry, monitoring, and deployment. Partner with platform, security, and data engineering teams to implement scalable data lakehouse oriented feature architectures ...