Company Description
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
AI & Machine Learning Systems
- 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.
Backend Engineering
- 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 proficiency in at least one backend programming language such as TypeScript/JavaScript, Python, Go, Java, or similar.
- 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:
- Tokenization and token usage
- Prompt design and evaluation
- Experience taking software from development into production.
- Strong understanding of software architecture and engineering best practices.
- Ability to reason about system reliability, scalability, performance, and cost.
- Strong problem-solving and debugging skills.
Nice to Have
- Experience building AI agents or agentic workflows.
- Experience with model evaluation, benchmarking, and AI observability.
- Experience with vector databases and semantic search.
- Experience with MCP or similar tool/context protocols.
- Experience with AI frameworks such as LangChain, LlamaIndex, Vercel AI SDK, or equivalent.
- Experience with model providers such as OpenAI, Anthropic, Google, or open-source models.
- Experience deploying or operating inference infrastructure.
- Experience with Kubernetes, Docker, serverless infrastructure, or containerized workloads.
- Experience with AWS, GCP, or Azure.
- Experience with event-driven architectures and distributed systems.
- Experience building developer-facing AI platforms or internal AI infrastructure.
Engineering Principles
We value engineers who:
- Build for production, not demos.
- Understand the fundamentals before reaching for abstractions.
- Prefer simple, maintainable architectures over unnecessary complexity.
- Treat AI as a software engineering problem, not just a prompting problem.
- Think about reliability, security, observability, latency, and cost from the beginning.
- Can investigate problems independently and understand systems end-to-end.
- Write clear, well-tested, maintainable code.
- Are comfortable working in ambiguous environments and turning ideas into working products.
- Continuously evaluate whether an AI solution is actually the best solution for the problem.
What Success Looks Like
In this role, you will:
- Build and ship reliable AI-powered features to production.
- Improve the quality, performance, and reliability of our AI systems.
- Establish patterns and infrastructure that allow the broader engineering team to build AI features efficiently.
- Help evolve our backend architecture to support increasingly sophisticated AI workloads.
- Reduce unnecessary AI costs and latency while improving user outcomes.
- Contribute to technical architecture and engineering standards across the organization.
- Become a key technical contributor in defining how AI is integrated into our products.
Education & Experience
A degree in Computer Science, Software Engineering, or a related field is beneficial but not required. We value demonstrated engineering ability and production experience.
We are particularly interested in candidates who can demonstrate what they have built, shipped, and operated, whether through professional experience, open-source contributions, or substantial personal projects.
