Head of AI Enablement
Coinbax is a programmable stablecoin payment platform that provides financial institutions with smart contract-based escrow, compliance controls, and fund reversibility.
About Coinbax
Coinbax provides financial institutions with a programmable control layer for stablecoin and tokenized deposit payments, addressing the lack of governance and reversibility on standard blockchain rails. Institutions can apply conditional fund release, multi-party approvals, automated sanctions screening, and time-based escrow to transactions via smart contracts. The platform operates on Base and Solana and integrates with core banking systems, supporting major stablecoins including USDC, USDG, RLUSD, and PYUSD.
Skills
About the Role
You design and implement an AI integration layer across software and operational technology stacks. You establish Claude Code standards, templates, and guardrails; build MCP servers, tool integrations, and agentic workflows; create self-service AI interfaces for code changes, documentation updates, and operational automations; develop internal AI assistants; establish governance and security frameworks; and measure AI adoption through metrics such as ticket deflection, pull request velocity, and cross-functional autonomy.
Requirements
- Deep hands-on experience with LLM APIs, prompt engineering, and agentic frameworks such as Claude, OpenAI, and LangChain
- Strong software engineering fundamentals
- Experience building internal tools, developer platforms, or automation systems
- Understanding of code review, CI/CD, and secure software development practices
- Ability to translate technical capabilities into organizational workflows for non-engineers
- Comfort operating in an early-stage environment with ambiguity and rapid iteration
- Background in fintech, payments, or blockchain infrastructure
- Prior work enabling low-code or no-code development for business teams
Responsibilities
- Design and implement the AI integration layer across software and operational technology stacks
- Establish Claude Code as the primary development paradigm with standards, templates, and guardrails
- Build custom MCP servers, tool integrations, and agentic workflows
- Create self-service AI interfaces for code changes, documentation updates, and operational automations
- Develop internal AI assistants with context on systems, processes, and institutional knowledge
- Establish governance, security, and review frameworks for AI-generated contributions
- Measure and optimize AI adoption using ticket deflection, pull request velocity, and cross-functional autonomy metrics