1 to 25 of 47 Amazon Bedrock Jobs in the UK excluding London

pipelines and ETL processes using state-of-the-art tools Skilled at architecting and managing secure, scalable AWS environments and working experience in the data & analytics services such as Amazon EC2, AWS Lambda, AWS Fargate, Amazon ECS, Amazon EKS, Amazon S3, AWS Glue, Amazon RDS, Amazon DynamoDB, Amazon Aurora, Amazon SageMaker, Amazon Bedrock (including LLM hosting and management). Expertise in workflow orchestration tools such as Apache Airflow Experience implementing DataOps best practices and tooling, including DataOps.Live Advanced skills in data storage and management platforms like Snowflake Ability to deliver insightful analytics via business intelligence tools such as Power BI Full-stack development experience: backend (Node.js, Python), frontend (ReactJS) Demonstrated … experience designing and implementing Generative AI solutions (chatbots, digital assistants, content generation, etc.) Hands-on implementation and operation of AI/ML models with services like Amazon SageMaker Advanced proficiency in Python and related AI/ML productivity libraries Expertise in SQL and NoSQL database technologies Skills Python AWS Gen AI Job Title: Technical Lead Location: Cambridge, UK Job More ❯

. Familiarity with AI/ML Ops pipelines , real-time analytics, or edge deployments. Big Data stack knowledge (e.g., Hadoop, Spark, Kafka). GenAI/LLM experience (e.g., AWS Bedrock, LangChain). Why this is a great move 🌳 Mission & impact: Work on projects where data-driven decisions have real-world consequences. Growth: Multiple openings from mid-level to Principal More ❯

. Familiarity with AI/ML Ops pipelines , real-time analytics, or edge deployments. Big Data stack knowledge (e.g., Hadoop, Spark, Kafka). GenAI/LLM experience (e.g., AWS Bedrock, LangChain). Why this is a great move 🌳 Mission & impact: Work on projects where data-driven decisions have real-world consequences. Growth: Multiple openings from mid-level to Principal More ❯

. Familiarity with AI/ML Ops pipelines , real-time analytics, or edge deployments. Big Data stack knowledge (e.g., Hadoop, Spark, Kafka). GenAI/LLM experience (e.g., AWS Bedrock, LangChain). Why this is a great move 🌳 Mission & impact: Work on projects where data-driven decisions have real-world consequences. Growth: Multiple openings from mid-level to Principal More ❯

insights and generate solutions from structured and unstructured data. Build data pipelines, models, and AI applications using cloud platforms and frameworks such as Azure AI/ML Studio, AWS Bedrock, GCP Vertex, Spark, TensorFlow, PyTorch, etc. Build and deploy production grade fine-tuned LLMs and complex RAG architectures. Create and manage the complex and robust prompts across the GenAI More ❯

insights and generate solutions from structured and unstructured data. Build data pipelines, models, and AI applications using cloud platforms and frameworks such as Azure AI/ML Studio, AWS Bedrock, GCP Vertex, Spark, TensorFlow, PyTorch, etc. Build and deploy production grade fine-tuned LLMs and complex RAG architectures. Create and manage the complex and robust prompts across the GenAI More ❯

insights and generate solutions from structured and unstructured data. Build data pipelines, models, and AI applications using cloud platforms and frameworks such as Azure AI/ML Studio, AWS Bedrock, GCP Vertex, Spark, TensorFlow, PyTorch, etc. Build and deploy production grade fine-tuned LLMs and complex RAG architectures. Create and manage the complex and robust prompts across the GenAI More ❯

life sciences, computer science, or a related field Experience with Agentic AI frameworks and tools (e.g., LangGraph, CrewAI, AutoGen) Familiarity with cloud native-AI infrastructure (e.g., Azure ML, AWS Bedrock, Snowflake) Prior experience in building AI products for clinical development or healthcare analytics RDSITAI IQVIA is a leading global provider of clinical research services, commercial insights, and healthcare intelligence More ❯

from strategic planning through to pre-production deployment and optimisation Architect and implement advanced solutions leveraging AWS's AI/ML services, with particular focus on Generative AI using Amazon Bedrock and SageMaker Provide technical leadership and mentorship to junior consultants while driving best practices across delivery teams Partner with customers to translate business challenges into measurable ML … candidates to apply. If your career is just starting, hasn't followed a traditional path, or includes alternative experiences, don't let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world's most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating - that's why customers from the most … software development life cycle (sdlc) and agile/iterative methodologies Experience in using Python and hands on experience building models with deep learning frameworks like Tensorflow, Keras, PyTorch, MXNet Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We More ❯

reliability and stability. Develop secure and scalable Python applications and RESTful APIs (FastAPI, Django REST Framework). Integrate AI services and foundation models from providers such as Azure AI , Amazon Bedrock , and Google Vertex AI . Work with vector databases and retrieval mechanisms to enhance accuracy and context. Collaborate with data, design, and engineering teams to improve model More ❯

reliability and stability. Develop secure and scalable Python applications and RESTful APIs (FastAPI, Django REST Framework). Integrate AI services and foundation models from providers such as Azure AI , Amazon Bedrock , and Google Vertex AI . Work with vector databases and retrieval mechanisms to enhance accuracy and context. Collaborate with data, design, and engineering teams to improve model More ❯

reliability and stability. Develop secure and scalable Python applications and RESTful APIs (FastAPI, Django REST Framework). Integrate AI services and foundation models from providers such as Azure AI , Amazon Bedrock , and Google Vertex AI . Work with vector databases and retrieval mechanisms to enhance accuracy and context. Collaborate with data, design, and engineering teams to improve model More ❯

Large Language Models via API or in a local context (e.g. HuggingFace transformers). Practical experience using managed AI services and foundation models from a major cloud provider (e.g., Amazon Bedrock, Google Vertex AI, Azure AI Services). Experience with a major conversational AI platform (Google Dialogflow, Amazon Lex, Rasa, or similar). A solid understanding of More ❯

solutions What You’ll Bring Strong background in software/platform/DevOps architecture with AI/ML exposure Experience in using Azure Open AI and/or AWS Bedrock Hands-on experience deploying agentic or autonomous AI systems in production. Proficiency in Python Excellent communicator with consulting or client-facing experience Knowledge of AI governance, interpretability, and bias More ❯

solutions What You’ll Bring Strong background in software/platform/DevOps architecture with AI/ML exposure Experience in using Azure Open AI and/or AWS Bedrock Hands-on experience deploying agentic or autonomous AI systems in production. Proficiency in Python Excellent communicator with consulting or client-facing experience Knowledge of AI governance, interpretability, and bias More ❯

solutions What You’ll Bring Strong background in software/platform/DevOps architecture with AI/ML exposure Experience in using Azure Open AI and/or AWS Bedrock Hands-on experience deploying agentic or autonomous AI systems in production. Proficiency in Python Excellent communicator with consulting or client-facing experience Knowledge of AI governance, interpretability, and bias More ❯

and improve retrieval pipelines (embeddings, chunking, hybrid search, ranking, feedback loops), owning schema design, latency, and relevance across vector databases. LLM integration: Connect and orchestrate large language models (OpenAI, Bedrock, etc.), manage prompts, tools, safeguards, and evaluation. Data pipelines: Ingest, clean, and transform structured and unstructured data; design efficient schemas (Postgres/NoSQL) for search and analytics. Frontend (React More ❯

and improve retrieval pipelines (embeddings, chunking, hybrid search, ranking, feedback loops), owning schema design, latency, and relevance across vector databases. LLM integration: Connect and orchestrate large language models (OpenAI, Bedrock, etc.), manage prompts, tools, safeguards, and evaluation. Data pipelines: Ingest, clean, and transform structured and unstructured data; design efficient schemas (Postgres/NoSQL) for search and analytics. Frontend (React More ❯

and improve retrieval pipelines (embeddings, chunking, hybrid search, ranking, feedback loops), owning schema design, latency, and relevance across vector databases. LLM integration: Connect and orchestrate large language models (OpenAI, Bedrock, etc.), manage prompts, tools, safeguards, and evaluation. Data pipelines: Ingest, clean, and transform structured and unstructured data; design efficient schemas (Postgres/NoSQL) for search and analytics. Frontend (React More ❯

and improve retrieval pipelines (embeddings, chunking, hybrid search, ranking, feedback loops), owning schema design, latency, and relevance across vector databases. LLM integration: Connect and orchestrate large language models (OpenAI, Bedrock, etc.), manage prompts, tools, safeguards, and evaluation. Data pipelines: Ingest, clean, and transform structured and unstructured data; design efficient schemas (Postgres/NoSQL) for search and analytics. Frontend (React More ❯

and improve retrieval pipelines (embeddings, chunking, hybrid search, ranking, feedback loops), owning schema design, latency, and relevance across vector databases. LLM integration: Connect and orchestrate large language models (OpenAI, Bedrock, etc.), manage prompts, tools, safeguards, and evaluation. Data pipelines: Ingest, clean, and transform structured and unstructured data; design efficient schemas (Postgres/NoSQL) for search and analytics. Frontend (React More ❯

and improve retrieval pipelines (embeddings, chunking, hybrid search, ranking, feedback loops), owning schema design, latency, and relevance across vector databases. LLM integration: Connect and orchestrate large language models (OpenAI, Bedrock, etc.), manage prompts, tools, safeguards, and evaluation. Data pipelines: Ingest, clean, and transform structured and unstructured data; design efficient schemas (Postgres/NoSQL) for search and analytics. Frontend (React More ❯

and improve retrieval pipelines (embeddings, chunking, hybrid search, ranking, feedback loops), owning schema design, latency, and relevance across vector databases. LLM integration: Connect and orchestrate large language models (OpenAI, Bedrock, etc.), manage prompts, tools, safeguards, and evaluation. Data pipelines: Ingest, clean, and transform structured and unstructured data; design efficient schemas (Postgres/NoSQL) for search and analytics. Frontend (React More ❯

and improve retrieval pipelines (embeddings, chunking, hybrid search, ranking, feedback loops), owning schema design, latency, and relevance across vector databases. LLM integration: Connect and orchestrate large language models (OpenAI, Bedrock, etc.), manage prompts, tools, safeguards, and evaluation. Data pipelines: Ingest, clean, and transform structured and unstructured data; design efficient schemas (Postgres/NoSQL) for search and analytics. Frontend (React More ❯

services powering AI features and data retrieval. RAG & Vector Search : Design and iterate retrieval pipelines using chunking, embeddings, hybrid search, and feedback loops. LLM Integration : Work with OpenAI/Bedrock models, orchestrate prompts/responses, and implement guardrails and evaluations. Data Engineering : Ingest and transform structured/unstructured data; design schemas for analytics and retrieval. Frontend (React/Next.js More ❯