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About the Role

Build the AI backbone of our product, including retrieval-augmented generation pipelines, multi-step agent workflows, embedding systems, and LLM integrations. Design vector search strategies, build agent loops, evaluate model quality, and ship production-ready systems. Bring a point of view on embedding models, chunking strategies, reranking approaches, and the tradeoffs between quality, latency, and cost.

Responsibilities

Design and build retrieval-augmented generation systems, owning chunking strategy, embedding selection, retrieval optimization, and reranking.
Implement and manage vector search infrastructure and integrate embeddings with our MongoDB core data layer.
Build multi-step agent loops with tool use, memory, planning, and guardrails, handling edge cases like hallucination and reasoning failures.
Integrate Claude and OpenAI APIs using orchestration frameworks, managing prompts, context windows, streaming, function calling, and tool use.
Build evaluation pipelines to measure LLM output quality and iterate on prompts, retrieval strategies, and model choices.
Use AI-assisted development as part of your workflow and help the team ship faster with AI tools.
Collaborate with product to scope AI features, advise on feasibility, and help set patterns and best practices.

Requirements

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.
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.
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).

Skills

Retrieval-Augmented Generation (RAG)
Vector Databases
Agent Workflows
LLM Integration
TypeScript
Python
Claude API
OpenAI API
LangChain
LlamaIndex
Pinecone
Weaviate
Chroma
MongoDB
Node.js
AWS
Prompt Engineering
Few-Shot Learning
In-Context Optimization
Fine-tuning
RLHF
Prompt Injection
AI Safety

Location

North York

Work Type

Full-time
In-office

Experience Level

Production Experience

Benefits

Competitive salary
Comprehensive health, dental, and specialist benefits
Company Macbook
Free parking and shuttle service to the office
Extra PTO during occasional US holidays
Company events, in-office restaurant, and building-wide perks
Unlimited ping pong and espresso

About the Company

STAN is the largest provider of AI solutions for HOA and Condo Property Managers.
We are using cutting-edge and patented artificial intelligence technology to build solutions that enhance life for residents of more than 4 million+ homes across North America.
Our mission is to build the world’s first AI property manager.
STAN is an award-winning platform, recognized by Rogers, FedEx, George Brown College, StartUp Canada, and the Waterloo Accelerator Centre.