Cloud AI Software Engineer (Full Stack) - New York - Hybrid

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 RNT 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
 

Platform Integrations and Tooling

Integrate the Test Hub with Jira APIs for defect linking, test case tracking, and AI-assisted sprint analysis
Connect with GitHub for pull request status, code change tracking, and AI-triggered test execution
Build or enhance test result dashboards with AI-generated summaries, historical trend reporting, environment health views, and failure analysis
Develop test artifact storage and retrieval patterns using S3 for logs, screenshots, reports, and agent execution outputs
Support onboarding of new QE teams and automation suites onto the Test Hub platform
 

Secondary Responsibilities — Quality Engineering (25%)

Test Automation Support

Develop and maintain automated API test suites for RNT Test Hub backend services using Pytest, REST-assured, or Postman/Newman
Build Playwright-based UI tests for critical Test Hub workflows and regression coverage
Integrate automated smoke, sanity, and regression suites into CI/CD pipelines for continuous validation
Write SQL-based data validation checks to verify backend data integrity, test result accuracy, and reporting correctness
 

QE Platform Reliability

Improve test execution stability by addressing flaky tests, strengthening assertions, and hardening test data setup and teardown
Instrument test execution with structured logging, retry logic, and failure categorization to improve triage speed
Support troubleshooting of failed test runs using CloudWatch logs, execution traces, and API response data
Help define and document test standards, patterns, and onboarding guides for teams using the Test Hub
 

Required Skills

Cloud and Application Development

7+ years of hands-on AWS experience with services including ECS/Fargate, ECR, S3, CloudWatch, IAM, VPC, Secrets Manager, Lambda, Step Functions, and ALB
Strong full-stack development experience: React or similar frontend framework, Python (FastAPI/Flask) or Node.js/Express backend, PostgreSQL or Aurora relational database
Proficiency in Docker: writing Dockerfiles, building images, managing container deployments on ECS
Experience building and maintaining CI/CD pipelines with GitHub Actions, Jenkins, GitLab CI, or AWS CodePipeline
Solid understanding of REST API design, HTTP, JSON, authentication patterns (JWT, OAuth2), and API versioning
Experience with infrastructure-as-code using Terraform or AWS CloudFormation
Strong Git practices: branching strategies, pull requests, code reviews, and release management
Working knowledge of SQL, schema design, and relational database operations
 

Agentic AI and Amazon Bedrock

Hands-on experience with Amazon Bedrock including model invocation, Bedrock Agents, Knowledge Bases, and action group development
Experience building agentic or LLM-powered workflows: multi-step reasoning, tool use, retrieval-augmented generation (RAG), and autonomous task execution
Proficiency in Python for AI application development: prompt construction, response parsing, agent orchestration, and LLM API integration
Understanding of prompt engineering principles, system prompt design, and techniques to improve model output reliability and accuracy
Experience integrating LLM agents with external tools and APIs via Lambda, REST endpoints, or MCP-style tool interfaces
Familiarity with vector databases, embedding models, and semantic search for knowledge base construction
 

Quality Engineering

Familiarity with test automation frameworks: Playwright, Pytest, REST-assured, or Postman/Newman
Understanding of QE concepts: regression, smoke, API contract testing, data validation, and test data management
Ability to write and maintain automated tests as part of standard development delivery
Experience reading and triaging test failures using logs, traces, and backend data
 

Preferred Skills

Experience with Amazon Bedrock Guardrails, model evaluation, or agent tracing and debugging
Experience with LangChain, LlamaIndex, or other agent orchestration frameworks
Familiarity with MCP (Model Context Protocol) for structured tool and API integration with AI agents
Experience with Snowflake, Redshift, or other cloud data warehouses as agent data sources
Familiarity with observability tools such as Dynatrace, Splunk, Grafana, or OpenSearch
Experience with Jira APIs or Atlassian tooling integrations
Experience in banking, financial services, compliance, or enterprise platform engineering
Experience building internal developer portals, QE platforms, or shared AI-powered tooling services
 

Responsibilities by Area

React Frontend
Build and maintain Test Hub portal, dashboards, and AI-powered QE interfaces
Backend Services
Develop FastAPI or Node.js APIs powering Test Hub workflows and agent orchestration
Agentic AI
Design and build Bedrock-powered agents for test generation, triage, and QE analysis
Database
Design Aurora/PostgreSQL schemas; own test result storage and reporting queries
AWS Infrastructure
Deploy and operate platform on ECS, S3, CloudWatch, IAM, Lambda, Bedrock
CI/CD Pipelines
Automate build, test, and deployment workflows via GitHub Actions or CodePipeline
IaC
Manage infrastructure with Terraform or CloudFormation for repeatable deployments
Integrations
Connect Test Hub with Jira, GitHub, Bedrock Knowledge Bases, and reporting tools
Test Automation
Build API and UI test suites; integrate into CI/CD for continuous validation
Platform Support
Onboard QE teams, document standards, and evolve Test Hub AI capabilities
 

Example Day-to-Day Activities

Design a Bedrock agent that reads a Jira story, extracts acceptance criteria, and generates a structured test scenario set
Build a Lambda-backed action group that allows a Bedrock agent to query Aurora for historical test failure patterns
Ship a new React dashboard panel showing AI-generated test run summaries and recommended actions
Develop a FastAPI endpoint that invokes a Bedrock agent to analyze an OpenAPI spec and flag contract drift
Write a Terraform module to provision a Bedrock Knowledge Base backed by an S3 test artifact store
Push a Docker image to ECR and deploy an updated Test Hub service through the CI/CD pipeline
Investigate a CloudWatch alarm on a Bedrock agent invocation failure and trace the execution path
Write a Pytest suite to validate a new backend endpoint and integrate it into the GitHub Actions pipeline
Pair with a QE lead to design an AI-assisted triage workflow that auto-categorizes test failures by root cause
Review a pull request adding a new agent action group and provide feedback on tool schema design and error handling
 

Success Measures

The person in this role will be successful when they:

Deliver reliable, production-grade features to the RNT Test Hub on a consistent cadence
Ship Agentic AI capabilities that measurably reduce manual QE effort and accelerate test cycle time
Maintain a stable, observable, and well-instrumented AWS platform with low incident rate
Reduce manual deployment effort through automated CI/CD pipelines and infrastructure-as-code
Enable QE teams to leverage AI-powered workflows for test generation, triage, and analysis
Improve test result visibility and reporting for QE leads and engineering leadership
Build quality into deliverables through integrated automated testing and clear documentation
 

Ideal Candidate Profile

The ideal candidate is a cloud-native software engineer who builds and ships full-stack applications on AWS, has hands-on experience developing Agentic AI workflows with Amazon Bedrock, and brings enough QE fluency to build quality into the platform from the ground up. They are comfortable owning infrastructure, CI/CD, application code, and AI agent design end-to-end.

This person is excited about the intersection of software engineering, cloud platform work, and applied AI. They understand that Agentic AI is not a prototype exercise — it requires the same rigor as production software: reliable orchestration, observability, testability, and clear human oversight. QE experience is a genuine differentiator for this role, not a checkbox.