Senior AI Engineer

Requirements

• 4+ years of professional software development experience with at least 2 years in full-stack roles.
• Experience with Node.js, Python, or Go for backend/API development.
• Working knowledge of PostgreSQL and at least one cloud data platform (Snowflake, Big Query, or Redshift).
• Experience with CI/CD pipelines and modern Dev Ops practices (Git Hub Actions, Harness, Jenkins, or similar).
• Familiarity with cloud platforms: GCP, Azure, or AWS (multi-cloud experience preferred).
• Demonstrated ability to read, review, and improve code generated by AI tools.
• Strong understanding of RESTful API design, versioning, and documentation.
• Comfortable working in Git-based collaborative workflows with branching strategies and pull request reviews.
• Hybrid/remote work flexibility. 3 Days in Office is a requirement.

• Experience with AI-assisted development tools such as Claude Code, Lovable, Cursor, or Git Hub Copilot.
• Familiarity with Supabase (PostgreSQL & Auth & Edge Functions) as a backend platform.
• Experience with Vercel for front-end deployment and serverless functions.
• Knowledge of vector databases and pgvector for AI/embedding workloads.
• Experience with Kong or similar API gateway platforms.
• Exposure to Kubernetes, Docker, and container orchestration on GCP or Azure.
• Understanding of AI agent architectures and how applications interact with LLM-based services.
• Experience with Dynatrace, Datadog, or similar APM/observability platforms.
• Familiarity with software supply chain security tools and practices (SBOM, image signing, Chainguard).
• Background in retail, automotive, or RV dealership technology systems is a plus.

We are seeking an AI Engineer to join our Technology & AI Organization. This role sits at the intersection of AI-assisted application development and full-stack engineering. You will take AI-generated front-end prototypes and transform them into production-grade, fully integrated applications—connecting user interfaces to backend services, APIs, databases, and enterprise systems.

You will work across the full software delivery lifecycle—from vibe-coded prototypes built in tools like Lovable and Claude, through Git Hub-managed source control, CI/CD via Harness, and deployment to Vercel, GCP, and Azure. This is a hands-on engineering role for someone who thrives at turning rapid AI-generated concepts into reliable, secure, scalable enterprise software.

• Take front-end applications generated through AI coding tools (Lovable, Claude Code, Cursor) and extend them into full-stack solutions with backend logic, API integrations, and database connectivity.
• Wire up front-end React/Next.js applications to backend services including Supabase (PostgreSQL), Snowflake, Salesforce APIs, and internal microservices.
• Implement authentication and authorization flows using Microsoft Entra  (Azure AD) SSO, OAuth 2.0, and RBAC patterns.
• Build and maintain RESTful and GraphQL API layers to connect UI components with enterprise data sources.

• Leverage AI coding assistants (Claude, Git Hub Copilot, Gemini Code Assist) to accelerate development while applying engineering judgment to AI-generated output.
• Review, refactor, and harden AI-generated code for production readiness—addressing security vulnerabilities, performance concerns, and maintainability.
• Collaborate with Agent Developers and AI Solution Architects to integrate AI agent capabilities into applications.

• Manage CI/CD pipelines in Harness for automated build, test, and deployment workflows.
• Deploy applications to Vercel (front-end/serverless), GCP (Kubernetes/Cloud Run), and Azure as required.
• Implement observability with Dynatrace, including application performance monitoring, log analytics, and alerting.
• Follow software supply chain security practices including container image scanning (Chainguard), secrets management (Cyber Ark), and dependency auditing.

• Design and optimize PostgreSQL schemas (with pgvector for AI/embedding workloads) in Supabase.
• Build data integration pipelines connecting Snowflake data warehouse to application layers.
• Work with Snowflake MCP connectors to enable AI-driven data access within applications.
• Ensure data integrity, query performance, and proper indexing across application databases.

• Configure and manage APIs through Kong/Konnect API gateway including rate limiting, authentication, and traffic management.
• Implement application security best practices including input validation, prompt injection prevention, and secure coding standards.
• Participate in code reviews with a focus on security and quality of AI-generated code.