AWS Architect // Reading, PA // Hybrid- Onsite

Required Qualifications

• Python (3.11+): Hands-on experience developing robust, scalable applications and implementing agentic logic using Python.
• Amazon SageMaker Unified Studio (SMUS) & MLOps: Hands-on experience with SMUS for managing the end-to-end machine learning lifecycle, including automated model training, model registration in centralized model registries, and configuration of real-time and asynchronous inference endpoints.
• Infrastructure as Code (IaC): Production-level expertise with Terraform for provisioning and managing AWS services, including Amazon Bedrock Agents, Knowledge Bases, DynamoDB, Lambda, and related cloud infrastructure.
• CI/CD & DevOps: Proven experience designing, implementing, and maintaining automated CI/CD pipelines for cloud-native applications and MLOps workflows.
• AWS Security & Identity: Strong experience with AWS IAM, including fine-grained permissions, access controls, and secure service-to-service communication.
• AWS Bedrock AgentCore: Familiarity with the AWS Bedrock AgentCore ecosystem, including Agent Runtime, Memory, Identity, and Tool Gateway capabilities.
• Multi-Agent Orchestration: Experience building or enabling collaborative multi-agent systems using frameworks such as LangGraph, Strands, or similar agent orchestration frameworks.
• Model Context Protocol (MCP): Strong understanding of MCP and modern tool/API integration patterns for AI agents and agentic applications.
• AWS Certification: AWS Professional-level certifications are preferred. AWS Certified AI Practitioner or AWS Certified Machine Learning – Specialty certification is highly desirable and considered a strong plus.
• Continuous Learning: Demonstrated willingness and ability to learn and adopt emerging AI technologies, agentic frameworks, AWS services, and cloud-native development practices.