Cloud Software Engineer

Role Summary

We are seeking a Cloud Software Engineer with quality engineering and Agentic AI experience to build, deploy, and operate the QE Test Hub platform on AWS. This is primarily a software development and cloud engineering role the engineer will own the design and delivery of full-stack applications including React frontends, FastAPI or Node.js backends, and relational databases, all deployed and operated on AWS.

A key and differentiating dimension of this role is the design and integration of Agentic AI capabilities powered by Amazon Bedrock. The engineer will build intelligent, autonomous agents that augment the Test Hub platform accelerating test generation, automating quality analysis, enabling AI-driven decision workflows, and reducing manual effort across the QE lifecycle.

Primary Responsibilities Cloud Development & Agentic AI (75%)

Full-Stack Application Development

• Design and build React-based frontend interfaces for the RNT Test Hub portal, dashboards, admin tooling, and AI-powered QE workflows
• Develop RESTful backend services using Python (FastAPI) or Node.js/Express to power Test Hub workflows and Agentic AI agent orchestration
• Design and manage relational database schemas using Aurora PostgreSQL or RDS; write optimized SQL for test result storage, reporting, and trend analysis
• Build reusable API layers, service integrations, and internal SDKs for Test Hub consumers and AI agent tool interfaces
• Implement authentication, authorization, role-based access control, and secure configuration patterns
• Deliver features end-to-end from design through deployment with clean, documented, production-grade code

Agentic AI Development Amazon Bedrock

• Design and build Agentic AI workflows using Amazon Bedrock, leveraging foundation models such as Claude (Anthropic) and Titan to power intelligent QE automation
• Develop multi-agent systems using the Amazon Bedrock Agents framework including agent definitions, action groups, knowledge bases, and tool invocations
• Build AI agents that autonomously perform QE tasks such as: test scenario generation from acceptance criteria, API contract analysis and drift detection, data quality assessment, test failure triage and root cause summarization, and regression impact analysis from code changes
• Integrate Bedrock agents with internal tools and APIs through Lambda-backed action groups, enabling agents to query databases, trigger test runs, read Jira tickets, and interact with GitHub
• Design Bedrock Knowledge Bases using S3-backed vector stores to give agents contextual awareness of test history, platform documentation, and QE standards
• Implement prompt engineering, system prompt design, and chain-of-thought patterns to optimize agent reasoning accuracy and output reliability
• Build human-in-the-loop approval workflows for high-impact agent actions using Step Functions and EventBridge
• Instrument agent executions with CloudWatch logging, trace capture, and performance metrics to support observability and continuous improvement
• Evaluate and iterate on agent output quality using structured test harnesses, ensuring agents produce reliable, actionable, and explainable results

AWS Platform Engineering

• Architect, deploy, and maintain the Test Hub on AWS using ECS/Fargate, ECR, ALB, VPC, IAM, S3, CloudWatch, Secrets Manager, Parameter Store, and Bedrock
• Containerize applications and services using Docker; manage image builds, versioning, and ECR lifecycle policies
• Design and implement CI/CD pipelines using GitHub Actions, Jenkins, or AWS CodePipeline for automated build, test, and deployment workflows
• Configure infrastructure-as-code using Terraform or AWS CloudFormation for repeatable, environment-consistent deployments
• Implement environment promotion patterns across development, QA, UAT, and production
• Set up CloudWatch dashboards, log groups, alarms, and metrics for platform and agent observability
• Manage IAM roles, policies, and permission boundaries for both platform services and Bedrock agent execution roles
• Integrate AWS Step Functions, Lambda, and EventBridge for orchestration of both platform workflows and agentic execution pipelines