Full Stack AI Engineer

Location: Remote - If based in NYC, will require office presence 2/3 days a week, for LATAM and European regions will be expected to work EST hours with a 5 hour overlap. Remote/Hybrid | Full-time Compensation: $140K - $200K

We are hiring on behalf of our client, an innovative technology firm who is seeking an experienced Full-Stack AI Engineer to join a growing, AI-focused engineering team. The client develops advanced intelligence and analytics solutions designed to help participants in digital asset markets make more informed decisions by combining financial data, automation, and modern AI technologies to transform complex information into actionable insights. In this role, the successful candidate will work closely with product, research, and engineering leadership to develop next-generation, AI-powered financial applications. This is a highly collaborative position that requires strong ownership, technical depth, and the ability to move quickly from concept to production.

Key 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.