Full Stack AI Engineer

Key Responsibilities

• Architect, develop, and maintain end-to-end applications powering AI-driven financial products
• Build scalable backend services and APIs that support intelligent workflows and automated decision-making
• Create intuitive, high-performance user interfaces that surface complex insights and enable interactive experiences
• Design systems that support real-time communication between users, data sources, and AI components
• Partner with research and machine learning teams to integrate AI capabilities into production environments
• Implement and maintain pipelines that ingest, process, and manage structured and unstructured financial data
• Support the deployment and operationalization of AI-powered features and workflows
• Establish testing, observability, monitoring, and reliability standards across applications and services
• Optimize system performance, scalability, and maintainability
• Evaluate and adopt emerging technologies across AI, software engineering, and financial infrastructure
• Stay informed on developments in large language models, agent frameworks, financial technologies, and modern web architectures
• Contribute to technical discussions and help shape engineering best practices across the organization

Requirements

• Experience: 5+ years of professional experience building full-stack applications
• Backend Expertise: Strong programming experience with Python, including modern API frameworks such as FastAPI. Advanced proficiency in JavaScript and TypeScript, with extensive experience developing applications using Node.js
• Frontend Expertise: Advanced proficiency in React for building user interfaces
• System Design: Demonstrated success building scalable web platforms, APIs, and backend services, alongside a strong understanding of both relational and non-relational database technologies
• AI & Agent Architecture: Deep knowledge of prompt design, tool-calling architectures, Model Context Protocol (MCP), and agent orchestration patterns. Experience deploying and operating AI agents or autonomous workflow systems in production environments
• Information Retrieval: Experience building embedding pipelines, semantic retrieval systems, and advanced search capabilities. Familiarity with vector search technologies, retrieval-augmented generation (RAG), and asynchronous application patterns
• AI Frameworks: Hands-on experience developing solutions using large language models and AI orchestration frameworks (e.g., LangChain or comparable technologies)
• Domain Knowledge: Experience working with financial datasets, market data, or financial service APIs
• Professional Attributes: Ability to operate comfortably in fast-moving environments with significant autonomy and ownership