AI Platform Lead with RAG Data Flow

• Strong experience in designing, building, and leading enterprise
• applications/platforms
• Deep expertise in AWS infrastructure and cloud technologies
• Strong hands-on experience with Terraform
• Exposure to AI/ML technologies (at least some practical experience is required)
• Strong leadership, communication, and stakeholder management skills

Scenario:Design an enterprise-grade AI platform on AWS that enables employees to query internal knowledge using a Retrieval-Augmented Generation (RAG) approach. The system should be secure, scalable, and compliant, leveraging AWS Bedrock for LLM capabilities.
Core Components:

• Frontend UI (EKS/ECS + ALB + SSO)
• Backend API and orchestration layer
• Hybrid knowledge layer (Vector DB + Graph DB)
• LLM inference using AWS Bedrock
• Strong security, compliance, and observability controls

Presentation Requirements:Candidates should present their solution using a (or equivalent) architecture diagram, covering:
Logical Architecture: High-level layers (UI, API, orchestration, knowledge, model, security, resiliency, availability)
Implementation Architecture: Mapping to AWS services (EKS/ECS, ALB,
Bedrock, OpenSearch, Neptune, S3, IAM, VPC, etc.)

Expectations During Discussion:

• Explain design decisions, trade-offs, and assumptions
• Demonstrate CI/CD, IaC, and platform engineering best practices
• Show end-to-end RAG data flow
• Map logical components to AWS services and networking
• Define security boundaries (VPCs, subnets, IAM roles, private
• endpoints)
• Highlight integrations (SSO, Bedrock, databases, storage)
• Address scalability (auto- scaling, stateless services, load
• balancing)
• Illustrate hybrid retrieval flow (vector + graph)
• Include observability and monitoring
• Clearly depict data flow