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This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Principal AI Engineer, Distributed Systems & Intelligent Platforms based in Canada.
This is a senior technical role focused on building and scaling intelligent, AI-native platforms operating at production-grade complexity. You will work on live, high-traffic distributed systems where reliability, performance, and architectural clarity are critical. The role blends AI engineering with distributed systems design, requiring deep expertise in backend development, cloud infrastructure, and event-driven architectures. You will contribute to building intelligent workflows, agentic systems, and scalable APIs that power next-generation learning and development platforms. The environment is highly collaborative, engineering-driven, and focused on solving complex real-world problems at scale. It is ideal for a hands-on principal-level engineer who thrives in deep technical ownership and production-critical systems.
Accountabilities:
In this role, you will be responsible for designing, building, and scaling distributed AI-driven systems and backend platforms that support high-traffic, production-grade applications.
- Design, build, and maintain backend services using Python and Node.js within cloud-native architectures
- Develop and evolve APIs and integration layers connecting frontend applications with distributed backend systems
- Architect and implement event-driven systems, including workflows, state machines, and asynchronous processing pipelines
- Contribute to advanced NoSQL data modeling, including scalable schema design, transactional patterns, and performance optimization
- Build and support AI-driven systems including LLM workflows, RAG pipelines, embeddings, and multi-agent orchestration
- Own production systems, including monitoring, debugging, observability, and continuous reliability improvements
- Collaborate with cross-functional engineering teams to deliver end-to-end features and improve system architecture
- Participate in code reviews, technical design discussions, and contribute to engineering best practices across the team
Requirements:
The ideal candidate brings deep experience in distributed systems, backend engineering, and production AI systems, with strong ownership of scalable architecture.
- 6+ years of professional software engineering experience building production-grade systems
- Strong expertise in Python and Node.js development
- Hands-on experience with AWS services such as Lambda, DynamoDB, S3, SQS, EventBridge, and Step Functions
- Strong understanding of event-driven architecture and distributed system design principles
- Advanced NoSQL data modeling experience, including composite keys and scalable single-table designs
- Practical experience with LLM-based systems, including RAG, embeddings, prompt engineering, and tool/function calling
- Experience designing and integrating RESTful APIs and backend services at scale
- Strong unit testing practices and commitment to maintainable, high-quality codebases
- Experience with containerization (Docker) and cloud deployment workflows
Nice to have:
- Experience reasoning about system-wide impact and cross-service dependencies
- Familiarity with AI observability tools such as Langfuse or similar platforms
- Experience optimizing AI systems for cost, latency, and performance
- Exposure to AI workflow orchestration using Step Functions or similar tools
- Experience in synthetic testing or evaluation of AI pipelines
- Strong ability to identify architectural gaps early in the design process
Benefits:
- Competitive compensation aligned with principal-level engineering roles
- Fully remote-first working model across Canada
- Opportunity to work on advanced AI-native and distributed systems at production scale
- High technical ownership with significant architectural influence
- Collaborative, engineering-led environment focused on deep technical excellence
- Exposure to cutting-edge LLM systems, agentic architectures, and cloud-native infrastructure
- Strong focus on learning, experimentation, and continuous improvement
- Opportunity to shape core platform architecture and long-term technical direction
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