AI Platform Engineer (Agentic Systems)
$218,400–$374,400 year
HybridChicago, Illinois, United States
Job Summary
Design and implement structured agent harnesses and orchestration frameworks that enable AI coding agents to operate reliably across enterprise codebases. Develop architectural constraints and guardrails, including dependency rules and custom linters, while building context engineering frameworks to enhance AI reasoning. Collaborate with engineering teams to integrate AI-driven development workflows into CI/CD pipelines and establish repository-as-source-of-truth models with machine-readable artifacts. Create multi-session workflows enabling incremental AI-driven development through progress tracking and implement automated feedback loops for continuous system improvement. This role supports Accenture's enterprise engineering teams in designing next-generation AI development systems.
Required Qualifications
- High School Diploma or GED equivalent
- Bachelor's degree in Computer Science, Engineering, or a related technical field
- 5+ years of experience in software engineering fundamentals, including architecture, CI/CD pipelines, automated testing, and developer tooling
- 1+ year of hands-on experience working with AI coding agents (e.g., Claude Code, Codex CLI, Cursor, Windsurf, or similar) in real-world development environments
- 1+ year of experience with agent orchestration or tool-use patterns, including MCP servers, APIs, or multi-agent frameworks
- Strong understanding of application architecture patterns and enterprise-scale development practices
- Access to reliable internet
- Ability to manage all responsibilities from a home office
- Must be available to work an 8-hour shift between 7:00 AM to 7:00 PM Eastern Time
- Monday – Friday work schedule
- Work authorization that does not now or in the future require sponsorship of a visa for work authorization in the United States
- No criminal conviction history
Desired Qualifications
- Experience building or contributing to developer platforms, internal tooling, or reusable reference architectures used across engineering teams
- Familiarity with context engineering, including structuring repositories, schemas, and documentation for effective AI agent reasoning
- Experience creating technical documentation, such as playbooks, runbooks, or developer guides
- Exposure to or proficiency with multiple programming languages or frameworks (e.g., .NET, Node.js, Python)
- Experience working in AI-first or agent-assisted development environments, where engineers define intent, constraints, and validation approaches
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