Lead AI Software Developer with Production Incident Exp

Position: AI Software Development Lead with Production Incident Exp

Location :Chicago, IL

Looking for a Senior/Lead AI Developer with hands-on technical lead experience responsible for triaging unfamiliar enterprise applications, designing the modernization approach, creating or evolving reusable AI skills/agents and guiding development pods through safe technology-stack and security upgrades while preserving the application's intended architecture and behavior.

Duties and Responsibilities:

10 Years of IT experience working as Software Engineer with production incident experience.

• 1-2 Years of experience working with agentic engineering and AI projects

. Lead reverse engineering of legacy and current-state applications using AI agents/skills to discover architecture, dependencies, integrations, database touchpoints, security flows, runtime assumptions, and deployment characteristics.

• Turn reverse-engineered findings into clear technical specifications and spec-driven implementation plans that can be executed by AI-assisted delivery pods.
• Design, author, refine, and govern reusable skills and agents for reverse engineering, dependency analysis, forward engineering, code modification, migration, remediation, and verification.
• Drive modernization of Java and .NET applications, including runtime/framework upgrades, dependency/JAR/package upgrades, application-server compatibility, and operating-system/infrastructure changes.
• Lead security modernization patterns, including identifying LDAP/legacy authorization logic and guiding migration to token-based identity and access patterns using Okta, OAuth/OIDC/JWT concepts as appropriate.
• Preserving existing application architecture where required, focus on making applications run safely on the target infrastructure rather than unnecessarily decomposing or re-platforming them.
• Assess blast radius across databases, interfaces, shared libraries, batch processes, downstream/upstream systems, configuration, and deployment pipelines before changes are executed.
• Break modernization work into pod-ready increments, assign work to developers, review agent outputs and code changes, and remain hands-on for complex or high-risk components.
• Continuously improve agent effectiveness across applications by capturing reusable patterns, failure modes, context requirements, prompts/instructions, and verification steps.
• Partner with AI Test Leads, architects, security, infrastructure, and application stakeholders to define acceptance criteria, quality gates, rollback considerations, and production readiness.
• Lead technical troubleshooting and root-cause analysis for complex failures and production-like issues; bring strong incident/P1 experience and disciplined systems thinking.
• Mentor developers in agentic engineering, spec-driven development, secure coding, code review, dependency management, and human-in-the-loop verification.