Full Stack AI Engineer

Responsibilities Full-Stack Product Development - Architecture & Maintenance: Architect, develop, and maintain end-to-end applications powering AI-driven financial products - Backend & APIs: Build scalable backend services and APIs that support intelligent workflows and automated decision-making - UI Development: Create intuitive, high-performance user interfaces that surface complex insights and enable interactive experiences - Real-Time Systems: Design systems that support real-time communication between users, data sources, and AI components AI & Data Integration - Cross-Functional Collaboration: Partner with research and machine learning teams to integrate AI capabilities into production environments - Data Pipelines: Implement and maintain pipelines that ingest, process, and manage structured and unstructured financial data - Operationalization: Support the deployment and operationalization of AI-powered features and workflows Engineering Excellence - Standards &

Reliability: Establish testing, observability, monitoring, and reliability standards across applications and services - Optimization: Optimize system performance, scalability, and maintainability - Technology Evaluation: Evaluate and adopt emerging technologies across AI, software engineering, and financial infrastructure Continuous Learning - Industry Trends: Stay informed on developments in large language models, agent frameworks, financial technologies, and modern web architectures - Best Practices: Contribute to technical discussions and help shape engineering best practices across the organization Requirements Required Qualifications - 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 Preferred Qualifications - Location: Located in the United States, Latin America, or Europe, with flexibility to collaborate across time zones - Performance Tuning: Strong understanding of applicati