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AWS Architect

Marici SolutionsReading, PA🇺🇸United States

Why this role stands out

Advance your career as an AWS Architect at Marici Solutions, where you will lead high-impact AI and MLOps initiatives while enjoying the perfect balance of hybrid flexibility. This role is an ideal match for a forward-thinking engineer with strong Terraform and SageMaker expertise who is eager to shape the future of cloud infrastructure and agentic AI. Apply today to join a collaborative team committed to technical excellence and your long-term professional growth.

Quick overview

SeniorityMid Senior
Work modeHybrid
LocationReading, PA, United States
DynamoDBAWSMLOpsPythonTerraformMachine LearningCI/CDLangGraphIAM

Job description

AWS Architect
Location : Reading, PA (hybrid)
Long Term Contract
Job description is below, and the key points are they must have direct experience with AWS SageMaker, Terraform, some AI familiarity is nice to have in particular AWS Bedrock, and they must be in Reading, PA or close enough to travel there on a regular basis.
Please include any of these certifications in your search requirements, along with Terraform and SageMaker; this might help filter for the right candidate.
  1. AWS Certified Machine Learning Engineer – Associate
  2. AWS Certified AI Practitioner
  3. AWS Certified Machine Learning – Specialty (MLS-C01)

Here is the revised JD

  • Python (3.11+): Hands-on experience building robust applications and agentic logic.
  • Amazon SageMaker Unified Studio (SMUS) & MLOps: Hands-on experience with SMUS to manage the end-to-end ML lifecycle. This includes automating model training, registering models in the centralized registry, and configuring real-time/asynchronous inference endpoints within the SMUS ecosystem.
  • Infrastructure as Code (IaC):Production-level expertise using Terraform to provision AWS services, including Bedrock Agents, Knowledge Bases, DynamoDB, Lambda, etc.
  • CI/CD & DevOps: Proven track record in building and maintaining automated deployment pipelines for cloud-native applications and MLOps workflows.
  • AWS Security & Identity: Deep experience with IAM for fine-grained permissions and secure service communication.
  • AWS Bedrock AgentCore : Familiarity with the latest ecosystem, specifically the Agent Runtime, Memory, Identity, and Tool Gateway layers.
  • Multi-Agent Orchestration: Experience building or enabling collaborative systems using frameworks like LangGraph, strands, etc.
  • Model Context Protocol (MCP): Strong understanding of MCP and modern tool/API integration patterns for AI agents.
  • AWS Certification (Optional): Holding one or more AWS Professional-level certifications—specifically the AWS Certified AI Practitioner or AWS Certified Machine Learning – Specialty—is highly preferred and considered a strong plus.
  • Continuous Learning: Willingness to learn emerging AI technologies, frameworks, and evolving cloud services.

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