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:
7-10 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.
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