AWS GenAI Solutions Architect

High Bridge Consulting LLC

. Select appropriate Amazon Bedrock foundation models and explain model-selection rationale .

Posted 10/9/2026full-timeRemote • United StatesMid-LevelSenior💰 $60 - $90 per hourWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in architecting and implementing GenAI solutions, including ingestion pipelines and document processing for structured and unstructured data. Proficient in AWS services, Terraform, and Python for developing production-quality components and securing sensitive data environments.

Highest-signal resume keywords

Amazon BedrockRAG Solutions DesignEKS/Kubernetes ArchitectureTerraform/Infrastructure-as-CodeAWS Security Architecture

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

GenAI SolutionsDocument ExtractionData Pipeline DevelopmentPython ProgrammingSemantic Layer ArchitectureHuman-in-the-Loop WorkflowsIngestion Pipeline OwnershipModel Integration for GenAIProduction DeploymentArchitecture Decision Records (ADRs)

Soft Skills

Presentation SkillsStakeholder Communication

Tools & Technologies

Amazon OpenSearchAmazon S3TerraformAWS CloudTrailAWS CloudWatch

Industry Keywords

Regulated Data SolutionsSensitive Data SecurityUnstructured Data ProcessingMixed-Format Document HandlingApproval Patterns

Tech Stack

Tools & technologies

AWSKubernetesPythonTerraform

About the role

Key responsibilities & impact

Select appropriate Amazon Bedrock foundation models and explain model-selection rationale
Design and implement RAG solutions with grounded, cited responses
Design agentic architectures with short-term and long-term memory
Implement human-in-the-loop workflows and approval patterns
Architect and own ingestion pipelines processing millions of mixed-format documents
Process PDFs with tables/images, CSVs, and other unstructured/semi-structured data
Design document extraction, parsing, preprocessing, chunking, metadata, and embedding strategies
Architect semantic layers across structured and unstructured data
Model and integrate different data shapes for GenAI/RAG applications
Design EKS/Kubernetes and container architectures and defend EKS versus ECS decisions
Implement Terraform-based infrastructure through production deployment
Design AWS networking, monitoring, logging, and production infrastructure
Design secure GenAI solutions for regulated or sensitive data, including IAM, encryption, VPC, secrets, guardrails, prompt-injection defense, PII masking, and CloudTrail/CloudWatch
Develop and troubleshoot production-quality GenAI and data-pipeline components in Python
Create ADRs and technical architecture documentation
Present and defend architecture decisions to client architecture review boards and senior technical stakeholders

Requirements

What you’ll need

Strong hands-on experience with Amazon Bedrock, including selecting appropriate foundation models and explaining model-selection rationale
Production deployment experience with Amazon Bedrock AgentCore / Agent Core
Experience with Amazon OpenSearch for vector search and semantic retrieval
Experience with Amazon S3 for data/document storage
Design and implementation experience with RAG solutions with grounded, cited responses
Experience with agentic architectures, including short-term and long-term agent memory
Experience implementing human-in-the-loop workflows and approval patterns
Hands-on ownership of ingestion pipelines processing millions of mixed-format documents
Experience with PDFs containing tables/images, CSVs, and other unstructured/semi-structured data
Strong understanding of document extraction, parsing, preprocessing, chunking, metadata, and embedding strategies
Ability to architect and own ingestion/data pipelines without relying on a separate data engineering team
Experience architecting a semantic layer across structured and unstructured data
Ability to model and integrate different data shapes for GenAI/RAG applications
Experience with EKS/Kubernetes and container architecture
Ability to explain and defend EKS versus ECS architecture decisions
Strong Terraform/Infrastructure-as-Code experience through production deployment
Experience with AWS networking, monitoring, logging, and production infrastructure
Strong 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 designing GenAI solutions for regulated or sensitive data environments
Strong Python skills for developing and troubleshooting production-quality GenAI/data pipeline components
Experience creating Architecture Decision Records (ADRs) and technical architecture documentation
Ability to present, explain, and defend architecture decisions to client architecture review boards and senior technical stakeholders

Benefits

Comp & perks

Full-time employment
Fully remote work
Compensation of $60.00–$90.00 per hour