The posting, in FloQast's own words
archived Oct 13, 2026As an AI Engineering Manager on our Core AI team in India, you will be a cornerstone of FloQast's AI transformation and a key leader in our growing global engineering organization. You will manage and scale a team of high-performing software and AI engineers responsible for architecting and deploying production AI systems—powering our accounting automation platform and bringing our vision of an AI accountant teammate to life. This role requires a balance between people leadership, operational excellence, and technical leadership in modern AI paradigms. You will partner with global engineering leadership, product management, and architecture teams to establish technical standards for agentic workflows, model context protocols (MCP), retrieval-augmented generation (RAG), and LLMOps across FloQast. Reporting to the VP of Engineering, you will play a critical role in expanding FloQast’s AI footprint by recruiting, mentoring, and developing top-tier engineering talent in India.
What You'll Be Doing
Team Leadership & Development: Lead, manage, mentor, and develop teams of AI software engineers. Grow your team members’ career paths through consistent coaching, performance management, and continuous feedback. AI Technical Direction & Delivery: Guide your team in the architecture and delivery of scalable production AI products, including intelligent chatbots, document processing systems, and agentic workflows using Python and modern AI frameworks. Recruiting & Site Growth: Actively recruit, interview, and retain top-tier AI engineering talent to build out FloQast's Core AI capabilities in India. Global Collaboration: Collaborate effectively with product managers, QA, architecture, security, and global engineering leaders to design and implement our centralized AI platform (model routing, provider management, vector search). Process & Culture: Cultivate an engineering culture of innovative, egoless, high-trust problem solving. Champion lean software development, Agile principles, and robust LLMOps/DevOps practices for AI systems. Production Excellence: Ensure your team builds robust, compliant, and observable AI products that integrate seamlessly with diverse technologies (accounting systems, external APIs) in a B2B SaaS environment.