AWS GenAI Solutions Architect

This is a fully remote position, open to applicants in United States.

📋 Description

• Choose suitable Amazon Bedrock foundation models and articulate the rationale behind your model selection.

• Create and implement RAG solutions that provide grounded, well-cited responses.

• Design agentic architectures that incorporate both short-term and long-term memory.

• Implement workflows that involve human oversight and establish approval patterns.

• Architect and manage ingestion pipelines capable of processing millions of documents in various formats.

• Handle PDFs with tables and images, CSV files, along with other unstructured or semi-structured data.

• Develop strategies for document extraction, parsing, preprocessing, chunking, metadata management, and embedding.

• Build semantic layers that integrate structured and unstructured data.

• Model and integrate diverse data formats for GenAI/RAG applications.

• Design EKS/Kubernetes and container architectures while justifying the choice between EKS and ECS.

• Implement infrastructure using Terraform, ensuring production deployment.

• Develop AWS networking, monitoring, logging, and production infrastructure.

• Create secure GenAI solutions for regulated or sensitive data, covering IAM, encryption, VPC, secrets management, guardrails, prompt-injection defense, PII masking, and CloudTrail/CloudWatch.

• Build and troubleshoot production-quality GenAI and data pipeline components using Python.

• Produce Architecture Decision Records (ADRs) and technical architecture documentation.

• Present and justify architectural decisions to client architecture review boards and senior technical stakeholders.

⛳️ Requirements

• Extensive hands-on experience with Amazon Bedrock, including the selection of appropriate foundation models and the ability to explain the rationale behind your choices.

• Proven experience in deploying Amazon Bedrock AgentCore / Agent Core in a production environment.

• Familiarity with Amazon OpenSearch for vector search and semantic retrieval.

• Experience with Amazon S3 for the storage of data and documents.

• Proven design and implementation skills for RAG solutions that yield grounded, cited responses.

• Background in agentic architectures, focusing on both short-term and long-term memory.

• Experience in the implementation of human-in-the-loop workflows and approval processes.

• Hands-on experience managing ingestion pipelines that process millions of mixed-format documents.

• Proficiency in handling PDFs with tables and images, CSVs, and other forms of unstructured/semi-structured data.

• Solid understanding of document extraction, parsing, preprocessing, chunking, metadata management, and embedding strategies.

• Capability to architect and manage ingestion/data pipelines independently, without reliance on a separate data engineering team.

• Experience in architecting a semantic layer across both structured and unstructured data.

• Ability to model and integrate various data shapes for GenAI/RAG applications.

• Familiarity with EKS/Kubernetes and container architecture.

• Ability to articulate and defend architectural decisions between EKS and ECS.

• Strong experience with Terraform and Infrastructure-as-Code through production deployment.

• Knowledge of AWS networking, monitoring, logging, and production infrastructure.

• Comprehensive understanding of AWS security architecture, including IAM/least-privilege access, KMS, TLS/encryption, private networking/VPC, Secrets Manager, Bedrock Guardrails, prompt-injection defense, PII detection/masking, and CloudTrail/CloudWatch.

• Experience in designing GenAI solutions suitable for regulated or sensitive data environments.

• Proficient in Python for the development and troubleshooting of production-quality GenAI and data pipeline components.

• Experience in creating Architecture Decision Records (ADRs) and technical architecture documentation.

• Ability to present, clarify, and defend architectural decisions to client architecture review boards and senior technical stakeholders.

🏝️ Benefits

• Full-time employment.

• Fully remote work options.

• Compensation ranging from $60.00 to $90.00 per hour.