AI Developer - LLM Features & AI Systems

STAN AI is the AI assistant for HOA and condo association property managers. We work with 20,000+ communities representing 2.5 million homes across North America, and we just closed our Series A. Our platform integrates natively with every major property management software in the category — Vantaca, CINC, Enumerate, eUnify, VMS, Caliber/FrontSteps, Buildium — which is why management companies are switching to us faster than this market has ever moved.

We're looking for an AI Developer to build the AI backbone of our product — retrieval-augmented generation pipelines, multi-step agent workflows, embedding systems, and LLM integrations that property managers rely on daily.

You'll work directly with product and engineering to ship AI features end-to-end: designing vector search strategies, building agent loops, evaluating model quality, and shipping systems that actually work in production. You won't just execute tickets — you'll bring a point of view on embedding models, chunking strategies, reranking approaches, and the real tradeoffs between quality, latency, and cost.

• RAG Pipelines: Design and build retrieval-augmented generation systems. Own chunking strategy, embedding selection, retrieval optimization, and reranking.
• Vector Databases: Implement and manage vector search infrastructure (Pinecone, Weaviate, or similar). Integrate embeddings with our MongoDB core data layer.
• Agent Workflows: Build multi-step agent loops with tool use, memory, planning, and guardrails. Handle edge cases like hallucination, context limits, and reasoning failures.
• LLM Integration: Integrate Claude and OpenAI APIs using orchestration frameworks (LangChain, LlamaIndex, or equivalent). Manage prompts, context windows, streaming, function calling, and tool use.
• Evals & Quality: Build evaluation pipelines to measure LLM output quality. Iterate on prompts, retrieval strategies, and model choices based on real data.
• AI Tooling & Developer Experience: Use Claude Code and modern AI-assisted development as part of your workflow. Help the team ship faster with AI tools.Collaboration & Architecture: Work with product to scope AI features and advise on feasibility. Help set patterns and best practices as the AI feature set grows.

Must Have:

• Hands-on experience building RAG systems in production (chunking, embedding, retrieval, reranking)
• Real experience with embedding models (OpenAI, Cohere, or open-source) and vector databases (Pinecone, Weaviate, Chroma, or similar)
• Experience building agent loops or multi-step reasoning systems (tool use, memory patterns, error handling)
• Familiarity with Claude API and/or OpenAI API — prompt design, function calling, streaming
• Strong TypeScript and Python — you write clean, maintainable, well-tested code
• Understanding of LLM limitations: hallucination, context windows, latency, inference cost, and real-world tradeoffs

Strong Assets:

• Experience with Claude Code or AI-assisted development workflows
• Knowledge of LLM evaluation frameworks (RAGAS, custom metrics, semantic similarity scoring)
• Side projects or portfolio demonstrating real AI work (not tutorials) — GitHub, demos, case studies
• Hands-on experience with orchestration frameworks (LangChain, LlamaIndex, or equivalent)
• Experience with multi-modal inputs or structured output extraction (JSON mode, schema validation)
• Background shipping AI features in a production SaaS environment (not just experiments)
• Familiarity with Stan AI stack: Node.js, TypeScript, MongoDB, AWS
• Understanding of prompt engineering, few-shot learning, and in-context optimization

Nice to Have:

• Fine-tuning or RLHF experience
• Contributions to open-source AI projects
• Experience in PropTech, FinTech, or operations software
• Knowledge of prompt injection risks and AI safety patterns
• Familiarity with vector database administration (indexing, cost optimization, scaling)