AI Full Stack Engineer

Build, maintain, and support AI-enabled applications for Finance Technology so the AI Lead can stay focused on architecture, priority delivery, and stakeholder alignment. This is a hands-on build-and-run role, not a research scientist role.

• Design, develop, and ship full stack features across the UI, APIs, and data access layers.
• Implement and extend AI-assisted workflows (chat, retrieval, document Q&A, copilots) using approved enterprise model access.
• Write clean, tested TypeScript and Python, with reusable components rather than one-off scripts.

• Own day-to-day maintenance: defects, small enhancements, environment issues, and user support.
• Prepare demos, test evidence, release notes, and handover packs so the AI Lead can review rather than rebuild.
• Track open items, follow up with testers and business users, and keep delivery status current.
• Raise risks early (security, data handling, reliability) instead of waiting for review.

• Maintain local and non-production environments, logs, and basic health checks.
• Add unit and integration tests for new work, and regression tests for fixes.
• Follow secure SDLC practice: no secrets in code, least privilege, and InfoSec-approved patterns only.
• Document how each application is run, supported, and recovered.

• About 3 years as a full stack developer, with production features owned end to end.
• Strong in one modern frontend stack: React and TypeScript (Next.js is a plus).
• Strong in one backend stack: Python (FastAPI or similar) or Node.js, plus REST APIs.
• Working SQL and comfort with APIs, JSON, authentication flows, and Git.
• Practical exposure to LLMs or AI features: calling model APIs, prompt flows, retrieval, or embedding search. Depth in research or model training is not required.
• Clear written communication and the discipline to close tickets, not only start them.

• FastAPI, React, and TypeScript used together on the same product.
• Cloud basics (AWS), Docker, and CI pipelines.
• Finance, reporting, or SAP-adjacent application support.
• Familiarity with RAG, vector search, or enterprise LLM gateways.
• Experience supporting a senior engineer or lead: taking maintenance, test, and documentation load.