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# AI Engineer

- **Company:** REQUEST TECHNOLOGY
- **Location:** Chicago, IL, United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Audit Trail, Cloud Computing, Continuous Integration, Distributed Systems, Python (Programming Language), Software Deployment, Software Engineering, SQL Databases, Systems Integration, AI Infrastructure, Large Language Models, Containerization, Information Technology, Api Design, Docker
- **Published:** September 18, 2026
- **Apply:** https://www.dice.com/job-detail/7e2ab9e6-675e-49cf-a86f-5acae21ab057

## About the Role

· Bachelor's degree in Computer Science, Engineering, or a related technical field; Master's preferred

· 4-7 years of software engineering experience, with production AI/LLM systems experience preferred

· Strong Python; proficient SQL; solid distributed systems and API design fundamentals

· Hands-on experience building LLM-powered applications or agentic systems

· Working knowledge of AI-specific risk areas (prompt injection, output validation, hallucination mitigation, data privacy), or demonstrated ability to build this expertise quickly

· Cloud infrastructure experience (AWS preferred); comfort with Docker, CI/CD, and containerization fundamentals

· Direct experience with Claude, the Anthropic API, or Claude.ai/Console

· Experience designing agent orchestration or Model Context Protocol (MCP)-based integrations

· Production experience operating LLM-powered applications or agentic systems at scale

## Description

· Own the design and delivery of agentic AI systems end-to-end, from architecture through production deployment

· Architect agent orchestration patterns, tool and context integration, and evaluation pipelines for reliability in a regulated environment

· Own AI infrastructure supporting AIRE's agents and applications on AWS, and contribute to the team's growing infrastructure self-sufficiency

· Implement technical safeguards against AI-specific risks, including hallucination detection, prompt injection defense, and output validation, in collaboration with company AI Governance function

· Build audit logging and human-in-the-loop checkpoints into agentic workflows so agent actions remain traceable and reviewable in a regulated environment


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