Principal AI Engineer, Distributed Systems & Intelligent Platforms

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.

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