AI Deployment Engineer @ IL(Hybrid) on FTE/Contract ||

This role focuses on preparing internally developed applications-often built using low-code or AI-assisted tools-for production deployment. The ideal candidate will operate at the intersection of DevOps, application engineering, and business enablement, helping ensure that partially built solutions are secure, scalable, and ready for enterprise use. This individual will work closely with non-technical personnel (such as business users, analysts, etc.) and technical, development teams to refine, harden, and deploy solutions effectively.

Key Responsibilities

• Assess internally developed applications for production readiness, including security, performance, and scalability
• Refactor and enhance partially built solutions (including low-code or AI-generated applications) to meet enterprise standards
• Support deployment processes, including CI/CD pipelines, environment configuration, and release readiness
• Identify and remediate common issues in AI-generated or prototype code (e.g., security gaps, incomplete logic, poor test coverage)
• Collaborate with business users to understand intent and translate solutions into production-ready implementations
• Establish best practices for deployment, testing, and operational readiness
• Document recurring issues and contribute to improving development and deployment standards

Required Skills & Experience

• 4+ years of experience in DevOps, software engineering, or production support roles
• Strong understanding of application deployment, CI/CD pipelines, and environment management
• Experience working with cloud platforms (AWS, Azure, or Google Cloud Platform)
• Familiarity with application security concepts (authentication, authorization, secrets management)
• Experience working with APIs, data integration, and backend services
• Strong communication skills with the ability to work directly with business users

Preferred Skills & Experience

• Experience supporting low-code/no-code or AI-assisted development environments
• Familiarity with AI tools and generated code patterns (e.g., Copilot, Claude Code)
• Exposure to containerization and orchestration tools (Docker, Kubernetes)
• Experience with monitoring, observability, or performance tuning tools