BesTech

AI Engineer Developer | BesTech

Resources by role — AI Engineer Developer

AI Engineer Developer

BesTech resources for AI Engineer Developers responsible for building the transformation-enabling user interfaces and backend services that bring AI projects from prototype to production.

What this role does

Key responsibilities

Full-Stack Integration of AI-Generated Applications

  • 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 ID (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

AI-Assisted Development Workflow

  • Operate within the company "Vibe Code" development workflow: Lovable>GitHub>Claude Code>Wiring>Harness>Azure/Vercel
  • Leverage AI coding assistants (Claude, GitHub Copilot, Gemini Code Assist) to accelerate development while applying engineering judgement 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 Solutions Architects to integrate AI agent capabilities into applications

DevOps & Deployment

  • Manage CI/CD pipelines to 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 image scanning (Chainguard), secrets management (CyberArk), and dependency auditing

Database & Data Integration

  • 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

API Management & Security

  • 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

What this role requires

Required qualifications

  • 4+ years of professional software development experience with at least 2 years in full-stack roles
  • Strong proficiency in JavaScript/TypeScript, React, and Next.js for front end development
  • Experience with Node.js, Python, or Go for backend/API development
  • Working knowledge of PostgreSQL and at least one cloud data platform (Snowflake, BigQuery, or Redshift)
  • Experience with CI/CD pipelines and modern DevOps practices (GitHub 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

Nice to have

Preferred qualifications

  • Experience with AI-assisted development tools such as Claude Code, Lovable, Cursor, or GitHub 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

The AI challenge for this role

What Success Looks Like

01

First 30 Days

  • Complete onboarding across the company's technology stack, tooling, and Vibe-Code workflow
  • Deploy your first AI-prototyped application through the full pipeline from GitHub to production
  • Establish access to Snowflake, Supabase, Harness, and all required development environments

02

First 90 Days

  • Own the full-stack integration of 2-3 AI-generated applications, connecting front-end prototypes to backend services and enterprise data
  • Contribute to code review standards for AI-generated code, identifying common patterns and pitfalls
  • Build at least one usable integration pattern (e.g., Entra ID SSO template, Snowflake data connector, standard API middleware)

03

First 6 Months

  • Become the go-to engineer for taking AI-coded applications from prototype to production
  • Contribute to internal library of full-stack templates and integration patterns used across the AI development program
  • Support the broader goal of deploying 12 AI agents by providing the application infrastructure and integration layer