Payam Siyahpoosh
I design and build scalable cloud and AI solutions in AWS.
New to Contra
Payam is ready for their next project!
This architecture is designed as a cost-optimized, intelligent AI inference gateway on AWS that dynamically routes requests to the most appropriate foundation model while maintaining low latency and operational efficiency. Incoming requests enter through Amazon API Gateway and are forwarded to a Lambda-based Router that performs request validation, normalization, and workload classification. A Token Optimizer reduces prompt size and removes unnecessary context before execution, minimizing model costs. The Model Selector Lambda acts as the decision engine, leveraging a Semantic Cache in DynamoDB to immediately serve previously answered or semantically similar requests and consulting CloudWatch metrics for real-time performance, latency, and utilization insights. Based on request complexity, cost targets, and response quality requirements, the selector routes traffic to the optimal model tier—Small (Claude Instant) for simple low-latency tasks, Medium (Claude 2) for balanced workloads, or Large (Claude 3) for complex reasoning. This multi-model orchestration pattern significantly reduces inference costs, improves response times, increases cache hit rates, and provides centralized observability, making it a scalable and production-ready architecture for enterprise generative AI workloads on AWS.
1
1
I created this architecture to provide an intelligent, scalable, and cost-efficient AI inference platform that dynamically routes user requests to the most appropriate model based on complexity, latency requirements, and available context. Incoming requests enter through Amazon API Gateway and are processed by Lambda-based routing and preprocessing services, which enrich queries using a vector-based knowledge repository and leverage a DynamoDB cache for frequently requested or precomputed responses. A central Model Selector service evaluates the request characteristics and directs it to either a Fast, Standard, or Advanced model tier, balancing performance, cost, and response quality. The generated output is then passed through a response optimization layer to ensure consistency and relevance before being returned to the user. Finally, comprehensive performance monitoring captures operational metrics, model effectiveness, latency, and system health, enabling continuous optimization, governance, and scalability across the entire AI platform.
1
1
I architected and implemented a secure, cloud-native AWS analytics and AI platform that combined modern web application hosting, serverless APIs, enterprise authentication, automated ETL pipelines, scalable business intelligence, and AI-driven insights. The solution leveraged AWS services including AWS Glue, Amazon Athena, AWS AppSync (GraphQL), AWS Lambda, Amazon RDS/Aurora, Amazon Cognito, Amazon CloudFront, Amazon Route 53, AWS WAF, Amazon Bedrock Agents, and Amazon QuickSight to enable secure data ingestion, transformation, querying, visualization, and intelligent workflow automation. The platform was designed using a private VPC architecture with Lambda-based microservices and integrated CI/CD pipelines using GitLab and AWS CDK. It incorporated enterprise-grade security controls including AWS KMS encryption, IAM policy management, AWS Secrets Manager, AWS Shield, and network-level protections to ensure compliance and secure access. The resulting system improved operational efficiency, enabled self-service analytics for business users, and provided a scalable foundation for AI-powered applications and real-time data access.
1
8
Architected and implemented an AI-powered legal knowledge and document intelligence platform on AWS to enable attorneys to efficiently search, retrieve, and analyze legal and enterprise data across multiple sources. The system integrated public legal data, client matter files, and enterprise repositories using AWS Lambda-based connectors and AWS Glue ETL pipelines, with Amazon S3 as the central data lake and Amazon OpenSearch Service for scalable indexing and semantic search. Built NLP-driven document processing and entity extraction using Amazon Comprehend, and implemented contextual AI-assisted legal search and response generation using Amazon Bedrock foundation models and knowledge bases exposed through a secure API layer via Amazon API Gateway. Designed event-driven workflows and orchestration using AWS Step Functions and Amazon EventBridge, with DynamoDB for state management and metadata storage. Implemented secure authentication and authorization using Amazon Cognito and IAM, delivering an enterprise-grade, scalable, and compliant legal AI assistant platform with full observability and operational monitoring.
1
7
I designed and implemented a scalable, event-driven AWS data analytics and machine learning platform that integrates multiple enterprise data sources into a secure cloud-native architecture. The solution leveraged services such as Amazon S3, AWS Lambda, API Gateway, DynamoDB, OpenSearch, Glue, ECS Fargate, and SageMaker to automate data ingestion, transformation, indexing, analytics, and machine learning workflows. I also architected secure API access with Cognito authentication, WAF, CloudFront, and Route 53, while enabling real-time dashboards, metadata indexing, and intelligent search capabilities. The platform was built with a serverless-first approach to improve scalability, operational efficiency, and cost optimization, while incorporating monitoring, logging, encryption, and CI/CD best practices to support enterprise-grade reliability and governance.
0
8
I designed and implemented a scalable AI powered AWS architecture focused on low-latency GraphQL services for global users while integrating enterprise knowledge sources and CRM workflows. The solution leveraged AWS Global Accelerator, AppSync, and Amazon Cognito and while AWS Lambda handled intelligent query orchestration and backend processing. I integrated Amazon Bedrock using Claude 3 Sonnet together with Bedrock Knowledge Bases, Prompt Management, and Flows to enable generative AI capabilities and enterprise knowledge retrieval from Amazon S3, SharePoint, and Confluence. In addition, I implemented event-driven CRM integrations using Amazon EventBridge and Lambda, and established operational monitoring and model evaluation pipelines with Amazon CloudWatch, AWS X-Ray, and Amazon SageMaker Pipelines to ensure observability, performance optimization, and continuous AI model evaluation across the platform.
1
13