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.
This position is based in
North York and is currently full-time in-office. This position is ideal for someone who thrives in a collaborative, high-energy environment and enjoys building teams in person.**
Tasks- 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.
RequirementsMust 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)
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!
Most "AI developer" roles mean adding a ChatGPT call to an existing feature. This is different. You'll be building the AI backbone of a product that property managers depend on daily to run their business. You'll make real architectural decisions: embedding models, retrieval strategies, chunking approaches, evaluation metrics. You'll see the results ship and hear directly from customers.
