Highlights: - OTE (On-Target Earnings): $173,250 - $187,000 - Location: US Remote (EST, CST) - Reports to: Director, Business Operations (Finance) About Fundraise Up We’re Fundraise Up - a global fundraising platform built to make donating to nonprofits fast, seamless, and accessible to all. Every month, our technology powers tens of millions of dollars in donations across the globe. We focus on innovation that directly impacts results: faster load times, higher conversion rates, global payment support, and accessibility-first design. Our platform is trusted by many of the world’s leading nonprofits, including UNICEF, the Alzheimer’s Association, and a wide range of global NGOs. With a 4.9/5 rating across top software review platforms, we’re recognized not just for our impact - but for the quality of the product we deliver. About The Role The Lead AI Engineer is a highly technical role dedicated to building and deploying production-level AI solutions at Fundraise Up. Partnering closely with the AI Product Lead, you will take strategic blueprints and turn them into scalable, robust technical realities. This role demands deep expertise in modern AI frameworks, from RAG-based systems to complex agentic architectures, ensuring that our technical deployments are secure, efficient, and deeply integrated into our operational workflows. What You'll Do - Agent Architecture Design: Define precise technical requirements for scalable agent architecture, including necessary reasoning frameworks, risk management systems, and knowledge retrieval interfaces required for complex decision-making. - End-to-End Build Capability: Serve as the hands-on technical lead capable of building, testing, and deploying both interim (e.g., RAG-based systems) and highly complex agentic AI solutions. - Technical Implementation: Work alongside strategy leads to ensure that the technical prerequisites for achieving fully autonomous agentic operations are met on the twelve-month roadmap. - Operational Scaling & Integration: Oversee the technical integration and scaling of deployed AI agents within specific operational workflows (e.g., automating specific GTM tasks). - Validation & Testing: Ensure seamless deployment with rigorous validation testing, monitoring agent performance, and iterating on technical builds to improve decision accuracy and reduce latency. About You - 5+ years in software engineering, AI engineering, or technical architecture within high-growth technology environments or AI startups. - Proven hands-on experience in the technical build and deployment of LLM-powered applications, RAG systems, and agentic workflows. - Deep understanding of the modern AI tech stack, prompt engineering, API integrations, and scalable deployment infrastructure. - Proven ability to execute complex technical work streams with limited oversight and high accountability. - Strong technical communicator; able to explain complex architectural decisions and technical trade-offs to non-technical strategic partners. - A practical, shipping-oriented approach — you are motivated by building reliable, production-ready systems that solve real business problems. Perks & Benefits Our compensation package includes comprehensive benefits and perks, meaningful equity, and a competitive salary: - Health, Dental, and Vision insurance covered at 100% for employees, 80% for employee plus dependents, and 70% for employees plus family. - FSA and HSA Spending Account. - 20 days of vacation, 5 sick days, 11 company holidays plus an additional 1 floating holiday. - 401(k) plan with company match. - 100% Company-paid short-term disability, long-term disability, basic life insurance and AD&D. - Paid parental leave (12 weeks for primary caregivers / 6 weeks for secondary caregivers). - Generous home office stipend to support your remote workspace. - Annual professional development stipend to support your growth (e.g., workshops, courses, and seminars). - Charitable giving program and paid volunteer time off with registered non-profits. We are an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate on the basis of race, color, religion, gender, sexual orientation, gender identity or expression, age, national origin, disability, or any other characteristic protected by applicable law in the countries where we operate.

• Design and implement end-to-end AI system architectures. • Develop backend services, APIs, and data pipelines integrating AI models. • Write and maintain Python code leveraging Azure cloud services. • Prepare, structure, and validate training datasets with production constraints. • Contribute to technical discussions and architectural decisions.

• Design and develop AI-powered solutions using Microsoft Azure services, including OpenAI, Cognitive Search, Vision, Document Intelligence, and Cosmos DB for vector storage. • Build and iterate on retrieval-augmented generation (RAG) pipelines and knowledge-grounded architectures, including GraphRAG and emerging techniques like OmniRAG. • Rapidly prototype AI applications tailored to specific client needs, such as document digitization, data vectorization, and content generation workflows. • Contribute to the architecture and implementation of advanced AI systems, including agent-based orchestration and integration with modern APIs and platforms. • Prepare and structure training data, including labeled inputs for both text and image-based models, with a focus on production readiness and model performance. • Evaluate and adopt new tools and frameworks based on AI/ML advancements, ensuring that implementations are stable and scalable. • Collaborate across teams to deliver high-impact, cloud-native applications using Python (especially Azure's AI SDK). Exposure to JavaScript/TypeScript or Node.js is considered an asset. • Apply familiarity with engineering documentation and visuals (e.g., diagrams, schematics) to enhance AI-driven workflows where applicable.

• Define precise technical requirements for scalable agent architecture, including necessary reasoning frameworks, risk management systems, and knowledge retrieval interfaces required for complex decision-making. • Serve as the hands-on technical lead capable of building, testing, and deploying both interim (e.g., RAG-based systems) and highly complex agentic AI solutions. • Work alongside strategy leads to ensure that the technical prerequisites for achieving fully autonomous agentic operations are met on the twelve-month roadmap. • Oversee the technical integration and scaling of deployed AI agents within specific operational workflows (e.g., automating specific GTM tasks). • Ensure seamless deployment with rigorous validation testing, monitoring agent performance, and iterating on technical builds to improve decision accuracy and reduce latency.