Artificial Intelligence Engineer

Zown is a buyer-first real estate platform operating across Canada and the U.S., focused on helping people achieve homeownership faster. Through its innovative model, Zown provides buyers with up to $25,000 toward their home purchase, funded through the commission the company earns, with no repayment required and full ownership from day one. The platform brings together salaried realtors, trusted lenders, 24-hour pre-approvals, and instant home showings to create a streamlined, low-stress homebuying experience. By reducing uncertainty and empowering buyers with transparent support and financial advantages, Zown is reshaping traditional real estate. Team members join a mission-driven environment aimed at putting more control and value in the hands of homebuyers.

About the Role We are looking for an AI Software Engineer to design, build, and scale intelligent software systems that power our products. This role sits at the intersection of artificial intelligence and backend engineering, requiring strong software engineering fundamentals alongside hands‑on experience building production AI applications.

You will work across the full lifecycle of AI‑powered features — from designing backend services and data pipelines to integrating and evaluating LLMs, building AI agents and workflows, and operating these systems reliably in production.

The ideal candidate is not simply someone who can integrate an LLM API. You should understand how production backend systems are designed, how distributed services communicate, how data flows through systems, and how to make AI applications reliable, observable, secure, and scalable as well as integrated with existing software systems.

What You’ll Do

Design and build production‑grade applications powered by LLMs and other AI technologies.

Integrate and work with modern AI models, APIs, and inference platforms.

Build AI agents, workflows, tool‑calling systems, and multi‑step reasoning pipelines.

Develop systems for retrieval‑augmented generation (RAG), embeddings, semantic search, and knowledge retrieval.

Design effective prompting, structured outputs, context management, and model orchestration strategies.

Evaluate AI system quality, including accuracy, reliability, latency, and cost.

Build automated evaluation and testing frameworks for AI‑powered features.

Stay current with developments in LLMs, agent architectures, model capabilities, and AI infrastructure.

Design and develop scalable backend services and APIs that support AI‑powered products.

Apply strong software engineering principles to produce maintainable, testable, and well‑structured systems.

Design data models, service boundaries, queues, background jobs, and event‑driven workflows.

Build systems that handle asynchronous and long‑running AI workloads reliably.

Work with relational and non‑relational databases, caching systems, object storage, and search infrastructure.

Design for concurrency, fault tolerance, retries, idempotency, and graceful failure.

Optimize application performance, infrastructure utilization, and AI inference costs.

Build secure systems with appropriate authentication, authorization, data protection, and access controls.

Production & Infrastructure

Deploy and operate AI and backend services in cloud environments.

Implement monitoring, logging, tracing, metrics, and alerting for AI‑powered systems.

Establish appropriate observability around model performance, latency, token usage, and costs.

Participate in architecture decisions and help establish engineering standards.

Troubleshoot complex production issues across application, infrastructure, data, and AI layers.

Collaborate with frontend, mobile, product, and infrastructure engineers to deliver end‑to‑end features.

What We’re Looking For Required

Strong professional experience in backend software engineering.

Strong understanding of:

API design and distributed systems

Databases and data modeling

Asynchronous processing and background jobs

Caching and messaging/queue systems

Authentication and authorization

Testing and software quality

Observability and production debugging

Hands‑on experience building applications using LLMs or other generative AI technologies.

Experience working with LLM APIs and understanding concepts such as:

Context windows

Tokenization and token usage

Structured outputs

Tool/function calling

Embeddings

Vector search

RAG

Prompt design and evaluation

Experience taking software from development into production.

Strong understanding of software architecture and engineering best practices.