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# AWS Agent Core Platform Engr- Hybrid

- **Company:** Generis Tek Inc.
- **Location:** Reading, PA, United States
- **Experience:** Expert
- **Contract:** Temporary to permanent
- **Skills:** Artificial Intelligence, Amazon Web Services, Confluence, Continuous Integration, DevOps, Amazon DynamoDB, Identity and Access Management, Python (Programming Language), Machine Learning, Language Modeling, Reliability Engineering, Cloud Services, Spring Cloud, Large Language Models, Grafana, Multi-Agent Systems, Infrastructure as Code (IaC), Agentic-AI, Amazon Bedrock, Deployment Automation, Machine Learning Operations, Functional Programming, Cloudwatch, Model Context Protocol, Terraform, Dynatrace
- **Published:** October 4, 2026
- **Apply:** https://www.dice.com/job-detail/29b9cdf7-bbef-40b3-a60b-1c66a9b21f74

## About the Role

* Python (3.11+): Hands-on experience building robust applications and agentic logic.
 * Amazon SageMaker Unified Studio (SMUS) & ML Ops: 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 ML Ops workflows.
 * AWS Security & Identity: Deep experience with IAM for fine-grained permissions and secure service communication.
 * AWS Bedrock Agent Core : 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 Lang Graph, 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.

Mandatory skills:

 * 8+ years in Platform Engineering, DevOps, or Site Reliability Engineering (SRE).
 * Cloud Expertise: Deep proficiency in AWS (IAM, CloudWatch, Bedrock, Lambda).
 * Observability Tools: Proven experience with Dynatrace, Jaeger, or Honeycomb, and distributed tracing standards.
 * AI/LLM Interest: Familiarity with the LLM lifecycle, including prompt execution, token usage, and frameworks like Lang Chain or Agent Core.
 * Automation: Advanced experience with Terraform and CI/CD pipeline design.
 * Collaboration: Experience working in an Agile environment with integrated tools like Microsoft Teams and Confluence.