1 to 25 of 96 Permanent Amazon SageMaker Jobs in London

that fuel analytics, AI/ML, and data-driven operations.* Design and build high-performance data integration solutions: Use AWS Glue, AWS Step Functions, Amazon EventBridge, and AWS Database Migration Service (DMS) to orchestrate ingestion, transformation, and movement of data across on-premises and cloud sources into Amazon … Design and oversee the delivery of secure lakehouse and warehouse architectures: Implement logical and physical data models, data quality controls, and governance practices using Amazon S3 (data lake), AWS Lake Formation, Amazon Redshift, and Amazon Athena to ensure trusted data.* Ability to design AI/ML-enabled ...

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 ...

Docker/Kubernetes). Strong background in data modelling, warehousing, and supporting analytical and ML workloads. Exposure to MLOps tools including MLFlow, kedro and Amazon Sagemaker. Development and maintenance of real time products like APIs using FastAPI/Flask. Operates autonomously with strong ownership and accountability. Strategic thinker ...

toolchains (e.g., LangChain, LlamaIndex, Semantic Kernel) and vector search (e.g., Pinecone, Weaviate, FAISS, Azure AI Search). Hands‐on experience with AWS Bedrock and Sagemaker would be highly desirable Experience with GenAI platforms (e.g., OpenAI API, Anthropic, Gemini, Llama or equivalents). Exposure to big data/distributed computing ...

skills Proficiency in coding in recent versions of Java and/or Python programming languages Experience with MLOps tools and platforms (e.g., MLflow, Amazon SageMaker, Google VertexAI, Databricks, BentoML, KServe, Kubeflow) Experience with cloud technologies (AWS/Azure/GCP) and distributed systems, web technologies and event drive … agentic system architectures, and Model Context Protocol (MCP) Familiarity with model serving frameworks (e.g., TensorFlow Serving, FastAPI) Exposure to feature stores (Feast, Databricks, Hopswork, SageMaker, VertexAI) Previous experience deploying & managing ML models is beneficial Experience working in a highly regulated environment or industry #ICBCareer #ICBEngineering #J-18808-Ljbffr ...

data structures, algorithms, machine learning, data mining, information retrieval, and statistics Knowledge of Kubernetes (AWS EKS) Experience with training models in Databricks and SageMaker Experience working with MLFlow Practical RAG experience - retrieval quality, embeddings, and vector stores; Graph RAG a strong plus Expert knowledge of at least ...

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 ...

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 ...

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 ...

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 ...

services, Infrastructure as Code (IaC, Terraform) and containerized application development Familiarity with data storage systems (e.g., Postgres, OpenSearch) and AWS services such as S3, SageMaker, and Bedrock Practical experience with Kubernetes, EKS, Docker, Kafka, MLOps, Large Language Model Operations (LLMOps), Event Driven Systems. #J-18808-Ljbffr ...

RFPs/tenders; experience running hands‐on product demos independently. Knowledge of cloud platforms (AWS, Azure, GCP) and AI/ML services (e.g., SageMaker, Vertex AI, AzureML), including sovereign, on‐premise, and hybrid deployment models. Understanding of LLM inference stacks (vLLM, llama.cpp, OpenVINO) and model delivery formats (ONNX, safetensors ...

. 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 ...

lifecycle. What you’ll be doing Build and maintain ML pipelines for training, evaluation, and deployment using tools like Databricks, MLFlow, Airflow, DBT, Sagemaker, Tecton Support AI scientist creating reproducible, containerized model training environments (on-demand and scheduled), and manage compute at scale (e.g., spot/GPU autoscaling) Define ...

experience of building data pipelines using Spark, Python or SQL - Experience working with AWS and its data and AI/ML services such as SageMaker - Experience of big data and lakehouse platforms on AWS (EMR, Databricks or Snowflake) - Experience building data platforms such as Data Lakes, Lakehouses or Data ...

streams Preferred Qualifications, Capabilities, and Skills Background in STEM with exposure to productionising machine learning systems Experience with MLOps tools and platforms (e.g., MLflow, Amazon SageMaker, Databricks, BentoML, Arize) Proficiency in cloud-native microservices architecture Hands-on experience with Amazon Web Services (AWS) Previous experience ...

with Cloud services, Infrastructure as Code (IaC) and containerized application development Familiarity with relational databases (e.g., Postgres) and AWS services such as S3, EKS, SageMaker, and Bedrock Practical experience with Kubernetes, EKS, Docker, Kafka, MLOps and LLMs #J-18808-Ljbffr ...

with serving patterns (e.g., REST APIs, batch inference, event-driven) Experience with orchestration tools (e.g., Airflow, Dagster) and cloud-native ML platforms (e.g., AWS SageMaker, GCP Vertex AI) Ability to influence decision-making regarding infrastructure and tooling, balancing 'build vs. buy' discussions Strong knowledge of Infrastructure as Code (Terraform ...

Expertise with modern data platforms (e.g., BigQuery/Databricks) and data processing workflows (ETL, pipelines). Excellent experience with cloud hosted AI platforms (Bedrock, Sagemaker, VertexAI) Strong problem-solving abilities, with the capacity to learn quickly and adapt in a fast-paced environment. Excellent communication and a drive ...

Migration tooling: AWS MGN, AWS DMS, AWS DataSync, AWS SCT, and AWS MAP framework Data and AI platform: AWS Glue, Kinesis, Lake Formation, Bedrock, SageMaker — for integrated data platform and agentic AI roadmap phases Apply Azure/GCP knowledge to extend solution coverage on multi-cloud or Azure-primary ...

everything that gets those models in front of a customer and keeps them running: the training and deployment infrastructure on AWS and SageMaker, reproducible and configuration-driven training runs, environments defined in code, release pipelines that make a deployment routine rather than an event, and the monitoring that tells … machine learning engineers/scientists and turn them into production services that meet our latency, cost and reliability targets. Build and maintain SageMaker training, processing and inference workloads, and the pipelines that orchestrate them. Make training runs reproducible and configuration-driven, so any result can be rebuilt from code. ...

machine learning engineers/scientists and turn them into production services that meet our latency, cost and reliability targets. Build and maintain SageMaker training, processing and inference workloads, and the pipelines that orchestrate them. Make training runs reproducible and configuration‐driven, so any result can be rebuilt from code. … PyTorch experience (TensorFlow also welcome) and the surrounding ecosystem: Hugging Face Transformers, timm, scikit‐learn, NumPy, pandas, OpenCV. Production experience on AWS, specifically Amazon SageMaker: training and processing jobs, pipelines, model registry, endpoints. Practical MLOps: experiment tracking and model versioning with MLflow, hyperparameter optimisation, and reproducible, configuration‐driven ...

with Cloud services, Infrastructure as Code (IaC) and containerized application development Familiarity with relational databases (e.g., Postgres) and AWS services such as S3, EKS, SageMaker, and Bedrock Practical experience with Kubernetes, EKS, Docker, Kafka, MLOps and Large Language Model Operations (LLMOps) Experience working on AIML systems and/ ...

optimization frameworks (AutoPrompt, DSPy) and building evaluation suites. Experience with distributed computing, data sharding, and performance optimization. Hands-on with AWS AI deployment services (SageMaker, Bedrock) and workflow orchestration. Demonstrated experience in financial services, particularly investment banking operations. #J-18808-Ljbffr ...

resolve blockers, and deliver results with a strong sense of urgency It’s a bonus if you have Experience with MLFlow/model versioning, SageMaker Pipelines, or Databricks Familiarity with real-time, high-volume data products and low-latency data stores (e.g. DynamoDB) Automated testing and validation frameworks ...