76 to 82 of 82 Remote Amazon Bedrock Jobs

Familiar with Terraform, from a developer perspective. Previous work in financial services, healthcare, or similarly regulated data environments. Experience with LLMs, voice agents, or tools like AWS SageMaker, AWS Bedrock, or ElevenLabs. Exposure to soft real-time systems or distributed system architectures. Knowledge of OAuth, SQS, or running containers in production. #J-18808-Ljbffr More ❯

and large-scale data sets and pipelines (e.g. AWS Glue, Redshift, RDS, Pinecone, Opensearch). Hands-on experience with AI Cloud Infrastructure for MLOps (e.g.Google Vertex AI/AWS Bedrock), including deploying AI applications and managing AI cloud-based services. Expert knowledge of REST, and proficiency in designing and writing APIs. Nice-To-Have Experience with Automatic Prompt Engineering More ❯

production-grade data infrastructure, preferably for ML/AI use cases with 1+ years working with LLM's. Proficiency with AWS data tooling and cloud-native services; experience with Amazon Bedrock or Claude integration is a plus. Experience with vector databases and embedding pipelines. Familiarity with or interest in graph-based data models and Graph RAG architectures is More ❯

curiosity, creativity, and continuous learning. What we are looking for Hands-On AI Experience : Proven track record building and deploying Gen AI solutions using modern cloud platforms (e.g. AWS Bedrock). Pattern Power : Deep understanding of RAG models, agent workflows, and multi-agent systems. Operational Know-How : Familiarity with GenAIOps, AI security, and responsible AI practices. Builder's Mindset More ❯

AI guidelines. Optimize model performance for latency, throughput, and cost-efficiency. Implement short-term and long-term memory modules to support persistent agent behaviours. Use cloud platforms such as Amazon Bedrock, Google Vertex AI, and Azure AI Studio for model orchestration and deployment. Build and integrate UI prototypes using tools like React for demoable outputs. Develop robust API More ❯

AI guidelines. Optimize model performance for latency, throughput, and cost-efficiency. Implement short-term and long-term memory modules to support persistent agent behaviours. Use cloud platforms such as Amazon Bedrock, Google Vertex AI, and Azure AI Studio for model orchestration and deployment. Build and integrate UI prototypes using tools like React for demoable outputs. Develop robust API More ❯

client workstreams and in the technology department as a whole. Technical skills and knowledge: Cloud hosting providers such as AWS, Azure, GCP, Vercel & Netlify AI platforms such as AWS Bedrock, Microsoft Azure AI, Google Vertex AI AI frameworks such as AutoGen, Langflow Fine-tuned and train AI models Knowledge of Infrastructure as Code, CI/CD Use of Middleware More ❯