FloQast
From FloQast's careers page
Status: listed on the employer's own careers page when we last checked (daily). Confirm on their site before applying.
As a Staff AI Engineer on our Core AI team, you will be a cornerstone of FloQast's AI transformation.
As a Staff AI Engineer on our Core AI team, you will be a cornerstone of FloQast's AI transformation. You will architect, build, and scale the AI products that power our accounting automation platform and enable our vision of an AI accountant teammate. This role requires deep expertise in production AI systems and a passion for solving complex accounting workflow challenges. You will not only build AI applications but also shape the technical direction of AI across FloQast, establishing the platform and standards that enable distributed AI execution company-wide.
Architect and lead development of production AI products including intelligent chatbots, document processing systems, and agentic workflows using Python and modern AI frameworks
Design and implement our centralized AI platform including model routing, provider management, vector search, and AI application frameworks with seamless MCP (Model Context Protocol) integrations
Build scalable AI products that integrate with diverse technologies including accounting systems, document repositories, and external APIs while maintaining robust monitoring and observability
Master context engineering and system design for AI applications, ensuring optimal information retrieval, context assembly, and multi-turn conversation management
Collaborate with Product, Engineering, and Security teams to ensure AI products are robust, compliant, and aligned with business objectives in the regulated accounting space
Mastery of Python, Familiarity with some AI application frameworks, context engineering, and scalable system design for AI products
Expertise in designing products that integrate with multiple technologies, APIs, and data sources in cloud-native environments (AWS preferred)
Strong desire to develop deep hands-on experience with LLM APIs, retrieval-augmented generation (RAG), conversational AI, document processing, and MCP integrations
Background in system integration, API design, or enterprise software platforms
Familiarity with accounting workflows and financial data processing
Experience with AI observability, debugging tools, and production AI monitoring
Hands-on experience with advanced RAG architectures, reranking systems, and retrieval optimization techniques
Knowledge of reinforcement learning from human feedback (RLHF) or other RL techniques for improving AI product performance
Experience building AI platform components like embedding pipelines, semantic search systems, or multi-modal processing frameworks
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