Applied AI Engineer, Splitero

• You will have a strong understanding of when supervised AI outweighs the risk of full automation
• You're as comfortable standing up a low-code n8n automation as you are writing clean code, and you know when each approach is the right one
• At Splitero, we move fast, and the ideal candidate thrives in that environment
• You default to the simplest path to a working solution, prototype quickly using every tool available to you, and have the engineering discipline to know when something needs to be rebuilt properly before it goes to production
• 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. You are on the bleeding edge of the latest releases and advancements in AI platforms
• 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
• Comfortable with AI-assisted development tools (Claude Code, Cursor, GitHub Copilot, etc.) to accelerate build cycles, with the judgment to write clean, maintainable code when it matters. You are not a vibe coder; you leverage your engineering background to balance speed with quality using AI-based tooling
• 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
• Self-directed and comfortable operating as a team of one. You run your own backlog and don't need a fully defined spec to get started and validate business use cases
• Strong communicator who can translate technical AI concepts for non-technical stakeholders across the org

Desirable

• 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
• In this role, you'll work directly with our sales, processing, underwriting, closing, and servicing teams to identify, scope, and execute opportunities to streamline and automate their workflows as we scale
• On the engineering side, you'll champion AI-first development, educate the team on emerging tooling, and lead technical initiatives that drive the organization forward
• You'll spend your time shipping working AI solutions into production by automating workflows, augmenting our product experience, and helping Splitero move faster and smarter as a business
• AI Discovery & Strategy:
• 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
• Stay current on the AI tooling landscape and recommend adoption where it meaningfully improves velocity, quality, or competitive positioning
• Building & Delivery:
• 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
• Integrate AI capabilities into existing systems and codebases in close partnership with the product and engineering teams
• Own the full AI development lifecycle, including prototyping, evaluation, deployment, monitoring, and iteration, with support from engineering, product, and operational teams
• Cross-Functional Partnership:
• Partner with product to translate user problems into AI-driven features and experiences
• Embed within operations and business teams to identify workflow automation and efficiency opportunities
• Collaborate with engineering to ensure AI integrations meet production standards for reliability, maintainability, and security
• Document and communicate AI implementations clearly so the broader team can support and build on them
• Reporting Structure:
• Reports directly to the VP of Product and Engineering
• Embedded within the product engineering team, works cross-functionally across Salesforce, product, operations, and business functions