Job description
Splitero is a fintech company that provides home equity investments, offering homeowners lump sum cash payments in exchange for a share of their home's future appreciation without requiring monthly payments or traditional credit qualifications. As Splitero's Applied AI Engineer, you will own the identification, scoping, and delivery of AI-powered capabilities across our product and operations. You'll sit within the engineering organization but operate cross-functionally, partnering with product, operations, and business teams to identify where AI creates real leverage, then build it. Splitero sits at the intersection of financial services and property technology, and our product and engineering teams are at the center of that innovation every day. Responsibilities: - Socialize AI capabilities, limitations, and roadmap across the org, acting as an internal resource and thought leader on what's possible. - Maintain a prioritized backlog of AI opportunities across the business, triaging ideas from leadership, product, and operations into scoped, deliverable workstreams. - Define AI initiatives end-to-end: problem framing, data requirements, build vs. buy decisions, and measurable success criteria. - Build, integrate, and maintain AI-powered features and workflows, leveraging LLM APIs (OpenAI, Anthropic, etc.), RAG pipelines, and agentic frameworks. - Develop automation workflows using low/no-code orchestration tools (n8n, Make) to deliver AI solutions rapidly without requiring full engineering cycles. - Partner with product to translate user problems into AI-driven features and experiences. - Collaborate with engineering to ensure AI integrations meet production standards for reliability, maintainability, and security.
Requirements
- 6+ years in software engineering with at least 2 years of focused experience building production AI or LLM-powered applications - Hands-on experience with LLM APIs, prompt engineering, retrieval-augmented generation (RAG), and agentic workflow design. - Demonstrated experience evaluating, advocating, and operationalizing AI tooling across an organization - Proficiency with low/no-code orchestration platforms (n8n, Make, Zapier) to prototype and deliver AI workflows rapidly - Comfort with AI-assisted development tools (Claude Code, Cursor, GitHub Copilot, etc.) to accelerate build cycles - Strong backend or full-stack engineering fundamentals; you integrate AI into real systems, not just prototypes - Fluency in Python, TypeScript/Node.js, or similar; familiarity with PostgreSQL and cloud deployment platforms (AWS, Vercel, or similar) - Experience with multi-agent frameworks (LangChain, LlamaIndex, or similar) - Familiarity with vector databases, embedding pipelines, and evaluation frameworks for LLM outputs