Sr. Data Engineer (GenAI & AWS) RemoteHybrid with Travel
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Monthly Salary:
Experience Required:
Posted:
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Job Summary
The key is time zone coverage and so we can expand our search across the West Coast - remote/hybrid but necessarily located in the western time zones. You must be comfortable to travel to the client site for workshops/meetings.
- Senior Data Engineer (GenAI / AWS)
Overall experience: 12-20 years Cloud Platform: AWS Primary Language: Python
Key Responsibilities
- Design and develop data pipelines using AWS Step Functions Lambda S3 Textract and Comprehend.
- Build automated document-processing workflows for extracting transforming and processing structured and unstructured data.
- Implement document chunking embeddings vector indexing and semantic search.
- Develop and maintain vector databases and knowledge bases to support Retrieval-Augmented Generation (RAG).
- Integrate knowledge bases with AI agents for contextual information retrieval.
- Build AI agents using Claude Agent SDK and deploy them on Amazon Bedrock AgentCore Runtime.
- Develop CI/CD pipelines using GitHub Actions to automate builds testing and deployments.
- Optimize data pipelines for scalability reliability security and performance.
Must-have vs. good-to-have skills
Must-have | Good-to-have |
Python SQL | TypeScript |
AWS Step Functions Lambda S3 | AWS Glue |
Amazon Textract and Comprehend | Document AI / OCR |
Vector databases and embeddings | OpenSearch Pinecone pgvector |
Document chunking and indexing | Advanced retrieval and reranking |
RAG and knowledge-base integration | LangChain / LlamaIndex |
Amazon Bedrock | Claude Agent SDK |
GitHub Actions CI/CD | Terraform / AWS CDK |
AWS IAM and API integrations | AgentCore Runtime |
Important: If this individual must personally build and deploy the Claude agents move Claude Agent SDK and AgentCore Runtime into the must-have category.
Required Skills:
Overall experience: 12-20 years Cloud Platform: AWS Primary Language: Python Key Responsibilities Design and develop data pipelines using AWS Step Functions Lambda S3 Textract and Comprehend. Build automated document-processing workflows for extracting transforming and processing structured and unstructured data. Implement document chunking embeddings vector indexing and semantic search. Develop and maintain vector databases and knowledge bases to support Retrieval-Augmented Generation (RAG). Integrate knowledge bases with AI agents for contextual information retrieval. Build AI agents using Claude Agent SDK and deploy them on Amazon Bedrock AgentCore Runtime. Develop CI/CD pipelines using GitHub Actions to automate builds testing and deployments. Optimize data pipelines for scalability reliability security and performance. Must-have vs. good-to-have skills Must-have Good-to-have Python SQL TypeScript AWS Step Functions Lambda S3 AWS Glue Amazon Textract and Comprehend Document AI / OCR Vector databases and embeddings OpenSearch Pinecone pgvector Document chunking and indexing Advanced retrieval and reranking RAG and knowledge-base integration LangChain / LlamaIndex Amazon Bedrock Claude Agent SDK GitHub Actions CI/CD Terraform / AWS CDK AWS IAM and API integrations AgentCore Runtime Important: If this individual must personally build and deploy the Claude agents move Claude Agent SDK and AgentCore Runtime into the must-have category.
