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## Job Summary
- Design and implement "loop engineering" workflows using Claude Code and similar tools, defining how agents plan, execute, test, and iterate on code changes
- Architect Model Context Protocol (MCP) servers and tool schemas that are legible and safe for LLM agents to use
Define human-in-the-loop checkpoints, autonomy boundaries, and rollback strategies for agent-driven changes
Build evaluation harnesses and observability practices to measure agent output quality and catch regressionsDesign and review microservice and microfrontend architecture using C4 diagrams and Domain-Driven Design principlesDrive test-driven development practices across teams, including contract testing between servicesOwn technical decisions across our Java microservices, Node.js services, React/microfrontend UI, and Python toolingDevelop responsive web applications using **React.js**.Build backend services and APIs using **Node.js** and Express.js.Integrate AI services and LLM APIs into web applications.Design and implement secure RESTful APIs and microservices.Develop dashboards for content management, assessment reporting, and learner analytics.## Key ResponsibilitiesWe are seeking a highly skilled **AI Engineer** who can define and scale how our engineering organization builds simulation software with AI agents. This role requires deep fluency in "loop engineering" with tools like Claude Code: designing the feedback loops, guardrails, and workflows that let AI agents safely and effectively contribute to production codebases. You'll need to understand the full agentic software development lifecycle, from context design through evaluation, deployment, and observability, while also being a hands-on expert across our core stack.
This is a staff-level role: you'll collaborate with other engineers to set technical direction, define best practices for agentic development across teams, and remain deeply hands-on in architecture and code
**Learning Content & Assessment Development**
- Design and develop hands-on lab activities, practical exercises, guided projects, and simulations.
- Create technical learning content for AI, cloud, software engineering, and emerging technologies.
- Develop assessment questions including:
- Multiple Choice Questions (MCQs)
- Scenario-based questions
- Coding exercises
- Capstone projects
- Knowledge checks
- Utilize Claude and other AI tools to generate, review, and improve educational content.
- Align content with learning objectives, competency frameworks, and certification standards.
**Generative AI**
- Claude (Anthropic), OpenAI, Azure OpenAI
- Prompt Engineering
- AI Agent Development
- RAG Architecture
- Vector Databases
- LLM Evaluation & Testing
- MCP and Tool Integrations
**Programming**
- JavaScript / TypeScript
- Node.js
- React.js
- Python
- REST APIs
- JSON
**AI Frameworks & Libraries**
**Databases**
- PostgreSQL
- MongoDB
- Redis
- Vector Databases
**Front-End Development**
- React.js
- HTML5
- CSS3
- Material UI / Bootstrap
- Responsive Design
**Backend Development**
- Node.js
- Express.js
- Microservices Architecture
- Authentication & Authorization
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