1 to 25 of 81 Amazon Bedrock Jobs

and maintaining tools that support data science and MLOps/LLMOps workflows. Collaborate with Data Scientists to deploy, serve, and monitor LLMs in real-time and batch environments using Amazon SageMaker, Bedrock Implement Infrastructure-as-Code with AWS CDK, CloudFormation to provision and manage cloud environments. Build and maintain CI/CD pipelines using GitHub Actions, AWS CodePipeline …/MLOps experience with a strong focus on building and delivering scalable infrastructure for ML and AI applications using Python and cloud native technologies Experience with cloud services, especially Amazon Web Services (AWS) - SageMaker, Bedrock, S3, EC2, Lambda, IAM, VPC, ECS/EKS. Proficiency in Infrastructure-as-Code using AWS CDK or CloudFormation. Experience implementing and scaling MLOps More ❯

and maintaining tools that support data science and MLOps/LLMOps workflows. Collaborate with Data Scientists to deploy, serve, and monitor LLMs in real-time and batch environments using Amazon SageMaker, Bedrock Implement Infrastructure-as-Code with AWS CDK, CloudFormation to provision and manage cloud environments. Build and maintain CI/CD pipelines using GitHub Actions, AWS CodePipeline …/MLOps experience with a strong focus on building and delivering scalable infrastructure for ML and AI applications using Python and cloud native technologies Experience with cloud services, especially Amazon Web Services (AWS) - SageMaker, Bedrock, S3, EC2, Lambda, IAM, VPC, ECS/EKS. Proficiency in Infrastructure-as-Code using AWS CDK or CloudFormation. Experience implementing and scaling MLOps More ❯

and maintaining tools that support data science and MLOps/LLMOps workflows. Collaborate with Data Scientists to deploy, serve, and monitor LLMs in real-time and batch environments using Amazon SageMaker, Bedrock Implement Infrastructure-as-Code with AWS CDK, CloudFormation to provision and manage cloud environments. Build and maintain CI/CD pipelines using GitHub Actions, AWS CodePipeline …/MLOps experience with a strong focus on building and delivering scalable infrastructure for ML and AI applications using Python and cloud native technologies Experience with cloud services, especially Amazon Web Services (AWS) - SageMaker, Bedrock, S3, EC2, Lambda, IAM, VPC, ECS/EKS. Proficiency in Infrastructure-as-Code using AWS CDK or CloudFormation. Experience implementing and scaling MLOps More ❯

by integrating vector databases (e.g., FAISS, Pinecone, OpenSearch). Perform LLM fine-tuning and prompt optimization for domain-specific use cases. Build and manage AI workflows on AWS SageMaker, Bedrock, and other cloud-native services. Develop clean, scalable, and reusable code using Python and modern ML frameworks (e.g. LangChain, Transformers). Collaborate with data engineers and MLOps teams to … retrieval strategies, embeddings, and semantic search. Hands-on experience in fine-tuning LLMs using Hugging Face, LoRA, or PEFT techniques. Strong familiarity with AWS AI/ML stack: SageMaker, Bedrock, Lambda, API Gateway, Step Functions. Knowledge of prompt engineering, embedding generation, and vector database integration. Exposure to deploying Gen AI models in containerized environments (Docker, EKS/ECS). … Familiarity with security and governance for LLM-based systems (e.g., PII filtering, access control). Experience using AWS Bedrock to deploy foundational models like Claude, Titan, or Command R. Knowledge of LangChain, LangGraph, or similar orchestration frameworks. Exposure to CI/CD pipelines, or MLOps workflows for LLMs. Understanding of Responsible AI principles and bias/fairness evaluation in More ❯

multi-class classification, decision trees, support vector machines, and neural networks (deep learning experience strongly preferred) Knowledge of popular Cloud computing vendor (AWS and Azure) infrastructure & services e.g., AWS Bedrock, S3, SageMaker; Azure AI Search, OpenAI, blob storage, etc. • Bachelor’s degree (master’s or higher strongly preferred) in machine learning, computer science, data science, applied mathematics or related More ❯

The AWS Tech Lead will have a strong background in cloud computing, specifically with Amazon Web Services (AWS) and will be responsible for leading the technical aspects of our AWS projects. The AWS Tech Lead will work closely with development and operations teams to ensure the successful delivery of cloud-based solutions. The role involves collaborating with cross-functional … Jira & Confluence) • Experience working on Migration/Modernization projects. • Understanding of networking concepts and protocols. • Experience with multi-cloud environments. • Exposure to AI/ML and Generative AI/Amazon Bedrock Certifications AWS Certified Solutions Architect – Associate/Professional. (Must More ❯

the requests library. Exposure to Lang Chain or MCP. Experience with one or more of the following AI Platforms: AWS Sage Maker Google Vertex AI OpenAI API Azure OpenAI Amazon Bedrock Cohere API Databricks Azure Machine Learning Replicate Hugging Face APIs Education: Bachelors degree in Computer Science, Software Engineering, or a related technical field. Other Information: CGI is More ❯

embedding technologies Expert in building RAG (Retrieval-Augmented Generation) systems at scale Strong experience with MLOps practices and model deployment pipelines Proficient in cloud AI services (AWS SageMaker/Bedrock) Deep understanding of distributed systems and microservices architecture Expert in data pipeline platforms (Apache Kafka, Airflow, Spark) Proficient in both SQL (PostgreSQL, MySQL) and NoSQL (Elasticsearch, MongoDB) databases Strong More ❯

embedding technologies Expert in building RAG (Retrieval-Augmented Generation) systems at scale Strong experience with MLOps practices and model deployment pipelines Proficient in cloud AI services (AWS SageMaker/Bedrock) Deep understanding of distributed systems and microservices architecture Expert in data pipeline platforms (Apache Kafka, Airflow, Spark) Proficient in both SQL (PostgreSQL, MySQL) and NoSQL (Elasticsearch, MongoDB) databases Strong More ❯

Delivery Consultant - Data Analytics and GenAI, AWS Professional Services Job ID: Amazon Web Services EMEA Dubai FZ Branch The Amazon Web Services Professional Services (ProServe) team is seeking a skilled Delivery Consultant to join our team at Amazon Web Services (AWS). In this role, you'll work closely with customers to design, implement, and manage AWS … 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 … Engineer Professional) preferred - Experience with automation and scripting (e.g., Terraform, Python) and kowledge of security and compliance standards (e.g., HIPAA, GDPR) - Experience with AWS AI/ML services (SageMaker, Bedrock, Amazon Q), SQL and at least one programming language (Python, Java, Scala) - Experience in AWS analytics & Generative AI services (e.g., Redshift, EMR, Glue, Athena, QuickSight, Bedrock, Q More ❯

Delivery Consultant - AI/ML and Generative AI, Professional Services GCC The Amazon Web Services Professional Services (ProServe) team is seeking a skilled Delivery Consultant to join our team at Amazon Web Services (AWS). In this role, you'll work closely with customers to design, implement, and manage AWS solutions that meet their technical requirements and business … analysis methodologies, and demonstrated ability to extract meaningful insights from complex, large-scale datasets PREFERRED QUALIFICATIONS - AWS experience preferred, with proficiency in a wide range of AWS services (e.g., Bedrock, SageMaker, EC2, S3, Lambda, IAM, VPC, CloudFormation) - AWS Professional level certifications (e.g., Machine Learning Speciality, Machine Learning Engineer Associate, Solutions Architect Professional) preferred - Experience with automation and scripting (e.g. … including fine-tuning, continuous training, small language model development, and implementation of Agentic AI systems - Experience in developing and deploying end-to-end machine learning and deep learning solutions Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity More ❯

AWS Technical Lead Job Description The AWS Technical Lead will bring a wealth of experience in cloud computing, particularly with Amazon Web Services (AWS), to guide the technical direction of our AWS projects. This role demands collaboration with development and operations teams to ensure the successful execution of cloud-based solutions. The AWS Technical Lead will engage with cross … Jira & Confluence. Experience working on Migration/Modernisation projects. Understanding of networking concepts and protocols. Experience with multi-cloud environments. Exposure to AI/ML and Generative AI/Amazon Bedrock. AWS Certified Solutions Architect - Associate/Professional (Must). Work Environment The work environment is collaborative and fast-paced, with a focus on utilising the latest technologies and More ❯

leadership An understanding of AI, and AI ethics An understanding of data safety in use of Large Language Models Knowledge and experience of either AWS or Azure: AWS (boto3, Bedrock, Sagemaker, Lambda, S3, EC2) Azure (azure Open AI service, Cosmos DB) Python Langgraph Neo4j/cypher Other coding languages/frameworks e.g. Java/.Net AI RAG (retrieval augmented More ❯

e.g. data and model versioning, model deployment CI/CD, MLFlow/DVC) Cloud platform experience, especially from an ML standpoint (AWS preferred) Statistical testing experience Experience with AWS Bedrock Experience with C# Containerization via Docker. Awareness of basic data science and generative AI methods. Exposure to generative AI application frameworks like LangChain, LlamaIndex, SmolAgents and griptape. What's More ❯

and RLHF Inference & Acceleration: Serve models using vLLM, DeepSpeed, Triton, TensorRT Multi-Agent Orchestration: Work with LangChain, AutoGen, CrewAI, DSPy and similar tools Cloud & MLOps (AWS): Deploy with SageMaker, Bedrock, Lambda, S3, ECS, EKS Full-Stack Integration: Build APIs (FastAPI, Flask) and integrate with React, TypeScript, Node.js Vector Search: Use FAISS, Weaviate, Pinecone, ChromaDB, OpenSearch Required skills & experience More ❯

and RLHF Inference & Acceleration: Serve models using vLLM, DeepSpeed, Triton, TensorRT Multi-Agent Orchestration: Work with LangChain, AutoGen, CrewAI, DSPy and similar tools Cloud & MLOps (AWS): Deploy with SageMaker, Bedrock, Lambda, S3, ECS, EKS Full-Stack Integration: Build APIs (FastAPI, Flask) and integrate with React, TypeScript, Node.js Vector Search: Use FAISS, Weaviate, Pinecone, ChromaDB, OpenSearch Required skills & experience More ❯

Systems : Develop and integrate agentic capabilities using frameworks such as LangChain, CrewAI, AutoGen, and DSPy. AWS Cloud & MLOps: Deploy scalable machine learning workloads on AWS using services like SageMaker, Bedrock, Lambda, S3, DynamoDB, ECS, and EKS. End-to-End AI Product Development : Work across the full ML lifecycle, from data collection and preprocessing to model evaluation, deployment, and monitoring. More ❯

Systems : Develop and integrate agentic capabilities using frameworks such as LangChain, CrewAI, AutoGen, and DSPy. AWS Cloud & MLOps: Deploy scalable machine learning workloads on AWS using services like SageMaker, Bedrock, Lambda, S3, DynamoDB, ECS, and EKS. End-to-End AI Product Development : Work across the full ML lifecycle, from data collection and preprocessing to model evaluation, deployment, and monitoring. More ❯

Systems : Develop and integrate agentic capabilities using frameworks such as LangChain, CrewAI, AutoGen, and DSPy. AWS Cloud & MLOps: Deploy scalable machine learning workloads on AWS using services like SageMaker, Bedrock, Lambda, S3, DynamoDB, ECS, and EKS. End-to-End AI Product Development : Work across the full ML lifecycle, from data collection and preprocessing to model evaluation, deployment, and monitoring. More ❯

Systems : Develop and integrate agentic capabilities using frameworks such as LangChain, CrewAI, AutoGen, and DSPy. AWS Cloud & MLOps: Deploy scalable machine learning workloads on AWS using services like SageMaker, Bedrock, Lambda, S3, DynamoDB, ECS, and EKS. End-to-End AI Product Development : Work across the full ML lifecycle, from data collection and preprocessing to model evaluation, deployment, and monitoring. More ❯

Systems : Develop and integrate agentic capabilities using frameworks such as LangChain, CrewAI, AutoGen, and DSPy. AWS Cloud & MLOps: Deploy scalable machine learning workloads on AWS using services like SageMaker, Bedrock, Lambda, S3, DynamoDB, ECS, and EKS. End-to-End AI Product Development : Work across the full ML lifecycle, from data collection and preprocessing to model evaluation, deployment, and monitoring. More ❯

Systems : Develop and integrate agentic capabilities using frameworks such as LangChain, CrewAI, AutoGen, and DSPy. AWS Cloud & MLOps: Deploy scalable machine learning workloads on AWS using services like SageMaker, Bedrock, Lambda, S3, DynamoDB, ECS, and EKS. End-to-End AI Product Development : Work across the full ML lifecycle, from data collection and preprocessing to model evaluation, deployment, and monitoring. More ❯

Systems : Develop and integrate agentic capabilities using frameworks such as LangChain, CrewAI, AutoGen, and DSPy. AWS Cloud & MLOps: Deploy scalable machine learning workloads on AWS using services like SageMaker, Bedrock, Lambda, S3, DynamoDB, ECS, and EKS. End-to-End AI Product Development : Work across the full ML lifecycle, from data collection and preprocessing to model evaluation, deployment, and monitoring. More ❯

of cloud architecture (AWS preferred), microservices, and distributed systems Experience integrating LLMs, AI/ML models, or building AI-powered workflows Bonus Points For: Hands-on experience with AWS Bedrock, Claude/Titan models, or custom LLM fine-tuning (DPO, PPO, RLHF) Experience with Neo4j, OpenSearch, vector-based search, or knowledge graphs Exposure to legacy modernization or domain-driven More ❯

and applications. Willingness to travel to client locations. Strong exposure to data engineering, ingestion, and ETL pipelines (SQL, PySpark, etc.). Expertise with cloud data platforms such as AWS Bedrock, AWS Redshift, or Databricks Lakehouse. Experience deploying AI systems (traditional ML and GenAI) into enterprise environments using Docker, Kubernetes, and API Gateway patterns. Knowledge of MLOps particularly integrated into More ❯