AI Agent Engineer - Cloud Infrastructure & Workflows

Orbis; McLean (VA); Hybrid; USD 170,000 - 240,000; Full time

Benefits offered by this job

Competitive retirement matching

PTO real-time off policy

Comprehensive benefits

Job summary

Orbis is building agentic AI capabilities to support mission operations. The AI Agent Engineer designs, builds, and deploys multi-agent workflows and the cloud platform across GCP, Cloudflare, and Azure, treating infrastructure as code and maintaining security and cost controls.

The role emphasizes explaining complex systems to stakeholders, collaboration with process owners, and ensuring reliable, scalable production deployments in a hybrid or remote-friendly environment.

Qualifications

• 5+ years translating user requirements into wireframes and workflows.
• 1+ year using Claude or similar to build AI agents.
• Experience building multi-agent workflows and explaining design decisions.

Responsibilities

• Design, build, and deploy agentic AI workflows and multi-agent orchestration.
• Explain system behavior to non-technical stakeholders and translate plain-language processes into workflows.
• Collaborate with process owners to ensure solutions fit operational needs and are production-ready.
• Maintain production reliability and diagnose failures across prompts, tools, data, and orchestration.
• Monitor and control AI workload costs and optimize for efficiency.

Skills

Wireframes & workflows

Claude AI agent usage

LangChain

GPT-class models

Prompt engineering

APIs & scripting

Troubleshooting AI workflows

Terraform / Pulumi

Cloud platforms (GCP/AWS/Azure/CloudFl

Security posture & IAM

Cost governance

Tools

Claude API

LangChain

AutoGen

CrewAI

Job description

Orbis is building agentic AI capabilities to support mission operations. The AI Agent Engineer designs, builds, and deploys multi-agent workflows and the cloud platform across GCP, Cloudflare, and Azure, treating infrastructure as code and maintaining security and cost controls.

The role emphasizes explaining complex systems to stakeholders, collaboration with process owners, and ensuring reliable, scalable production deployments in a hybrid or remote-friendly environment.