Full-stack development — can build a functional web UI with a database backend (not just scripts). Needs to handle front-end and back-end independently
AI/LLM integration — experience connecting to LLM APIs (Anthropic, OpenAI, Bedrock, Vertex). Understands prompting, token management, and agent architectures
Containerization — Docker or equivalent. Comfortable spinning up, configuring, and managing containerized applications
AI-assisted development — actively uses Claude Code, Cursor, or similar AI coding tools. This is non-negotiable — the team builds with AI
Database proficiency — SQL databases, Supabase, or equivalent. Can design schemas, write queries, and configure connections
API integration — REST APIs, authentication flows, webhook configuration, MCP server setup
Git/GitHub — clean commit history, branching, PRs
Cloud infrastructure experience — hands-on with at least one of: AWS, Azure, Google Cloud. Comfortable interacting with servers, databases, networking, and security groups
Core Responsibilities
1. Agent Builds (Primary)
Spin up containers (sandboxed environments) for AI agents
Configure MCP connections, API integrations, and data connectors
Write and refine agent prompts based on PRD specifications
Connect agents to client data sources (databases, CRMs, email, cloud storage)
Run 7-day minimum QA tuning periods — catch edge cases before production
Deploy agents to production and monitor post-launch
Debug and resolve post-production issues
2. Client Onboarding Support
Determine client cloud platform and existing infrastructure
Document required connections, data sources, and access permissions
Submit and track API key / account access requests
Test access grants to confirm correct permissions (read, read/write, admin)
3. Internal Tools & Maintenance
Maintain and improve Gbrain (company knowledge base)
Support Distill (AI news briefing tool)
Contribute to internal playbook and wiki documentation
Evaluate new tools and frameworks during allocated R&D time